{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "267e9d8b-c30b-4af0-a8b6-ccac46020de0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模拟订单数据条数： 1200\n",
      "   id                 internal_order_number  \\\n",
      "0   1  46685257-bdd6-40fb-8667-1ad11c80317f   \n",
      "1   2  96da1dac-72ff-4d2a-b86e-cbe06b65a6a4   \n",
      "2   3  759cde66-bacf-43d0-8b1f-9163ce9ff57f   \n",
      "3   4  5d65a441-d588-42de-a2bc-372f7412b293   \n",
      "4   5  c4b032cc-d7c5-44a5-9304-317faf42e12f   \n",
      "\n",
      "                    online_order_number store_name  \\\n",
      "0  bc8960a9-23b8-41e9-b924-56de3eb13b90       旗舰店A   \n",
      "1  01a9e71f-de8a-474b-8f36-d58b47378190       直营店C   \n",
      "2  ec1b8ca1-f91e-4d4c-9ff4-9b7889463e85       直营店C   \n",
      "3  35a240ae-5af3-4553-9ec4-2e0829a3b2e9       旗舰店A   \n",
      "4  d261a7ab-3aa2-44f9-8e51-f30dc6a7ee39       直营店C   \n",
      "\n",
      "                   full_channel_user_id       shipping_date  \\\n",
      "0  e465e150-bd9c-46b3-ad3c-2d6d1a3d1fa7 2022-05-09 08:56:41   \n",
      "1  b2b9437a-28df-4ec4-8e4a-2bbdc241330b 2022-04-20 22:36:40   \n",
      "2  4b0dbb41-8d52-48f1-942c-3fe860e7a113 2022-02-25 13:45:23   \n",
      "3  efc89849-b3aa-4efe-8458-a885ab9099a4 2022-02-26 08:27:03   \n",
      "4  66b2bc5b-50c1-47fc-8e17-7b4e0837b8a3 2022-02-16 03:28:51   \n",
      "\n",
      "         payment_date  payable_amount  paid_amount status  ... unit_price  \\\n",
      "0 2022-05-08 02:56:41             156       131.67    已完成  ...        156   \n",
      "1 2022-04-18 07:36:40             396       359.51    已收货  ...        132   \n",
      "2 2022-02-23 06:45:23             435       388.63    已完成  ...        145   \n",
      "3 2022-02-25 07:27:03             524       475.40    已收货  ...        131   \n",
      "4 2022-02-15 14:28:51             122       113.51    已完成  ...        122   \n",
      "\n",
      "  product_name color_and_spec product_amount original_price is_gift  \\\n",
      "0           口红            滋润型            156            181       否   \n",
      "1           口红             红色            396            169       否   \n",
      "2           口红            自然色            435            161       否   \n",
      "3           口红             红色            524            169       否   \n",
      "4           口红            清爽型            122            145       否   \n",
      "\n",
      "  sub_order_status refund_status registered_quantity actual_refund_quantity  \n",
      "0              已发货         未申请退款                   1                      0  \n",
      "1              已发货         未申请退款                   3                      0  \n",
      "2              已发货         未申请退款                   3                      0  \n",
      "3              已发货         未申请退款                   4                      0  \n",
      "4              已发货         未申请退款                   1                      0  \n",
      "\n",
      "[5 rows x 31 columns]\n",
      "\n",
      "由模拟数据汇总得到的销量：\n",
      "   商品  实际销量\n",
      "0  口红   653\n",
      "1  精华   714\n",
      "2  防晒   856\n",
      "3  隔离   648\n",
      "4  面膜   523\n",
      "5  面霜   785\n",
      "\n",
      "校验通过：汇总结果与目标表完全一致！\n"
     ]
    },
    {
     "data": {
      "image/png": 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83jiZ9kmzVmHN5gNyz4nwvivKkJu7dAv2HDwrt688TRpUx6aVM2Tqf6VVtlRR3LmwHaMnL1ZYk6dbh2ZYPn+8TPbb81fv0LG3I3z8lFtC5vXFFz0dpuDMgVVCva46NSpi+viBcFq4UdTXzNQIh3cuFgWTzl66jWHjMn6es9OIQd2FnxMTE+G0YIPcpUg2ZYqhXasftYBMjA0xc+IgAEDt6uLlrM0a1RQCvleuP8Dwgd2EY+mfs/Q0NDR+665v8gSFhGXZe0K3js2xfa1TloyVlVILdvv5ByEyMlrmuIG+Hr589fst105IkKLnwCm4cXaLENROSJBiy67j2VrjiYiIsg4DSkRE2ejE2euZ9nn6XLa2hZnpjyydVk1ri5ZDAcDug2dlCndfunYf3wKChTooAFC/dmVoa2uJsqIyWwYFAE9fvJUJKKWdU6oxkxdnOE5SUhJevHovE1BKO5a5mTGO71mGKhXFuxAFBYehXc8xcoNJf4Oa1WyUrgnzwvU9qjfunWGfRSt3oGSxgmjRpDYGjnTC6Qu3MuwPpCzvmjBzBT5+9sbi2WOwc/+ZTINJdt3bYO3iSTLLJ9dvPaQwmJQVWjWrIxNMevn6AybOXIG1SyYLH4wN9PWwZdVM9OzcEtPnrsWT76+PGlXKwWnKEJnfSwA4cvIyHEbPQUyM7DLKVGcPrUbDulUzneekMf0xaUz/zO+naR25BZxV2eJ8UL9OaNNctih5RnLlUByQS1W8qDVsW9YXHr997ylksaT19t1HlC1dVKn7BYDG9auhcf1qco9lFlD6E+ITEhAZ9SOoEh0dk2VjS6VS0diRUZmP3cN+Mo4rsTNl+mLiuw+egcOoOUrNKzUr9aOnt8yx6OhYhISG48adx1izSf5ru0jh/GjeqKbw+MnzN7j/8KXcvq5v3WXaIiKj0LG3I26d3wZNDQ30HDgF1249VGruRET092NAiYjoL2diYijTFhYRKfwsr8irvDoUycnJePTUVVRnQ0tLE0UL5Zf7QSDDORkbybSFp5mTSmPJub+0Y011tJcJJsXFxaNr/wn/dzsGDR47F3mscuLjJ9kPhxnZsO0IXB67ys0US8sypzlWLZggE0w6e+k2xk1fruCsrDFu2jKUKVEEtaqXBwBcvHYPvR2mISIyCvVaDcCBrQuFYwDQoE4V3LmwA8fPXIOWliZaNa0jM2ZEZBTGT1+ucBfCv511/txy66j9qi7tm0IikQiPy5QsglP7ZQtnj5iwIMOaUT+jYIE8QsaXTe0uWTp2esWKFAAABAaFIDgkHJt2HMWmHUd/y7WOnLyCIyev/JaxQ8MjRI/TL4GuWrEMdHW1cTPd7oXa2lrIn9cSAODuKfteWadlP9SqVh42ZYrj3OU7cq9dN6qiKKDk4emtsC+Q8rq89/CF6EuKT14+6NJnPELDI+H23lPhuURE9O9hQImI6C/XtEENmba0NWRKlSgkc/zNu49yx3rv/hloJm4rXrSASgElDQ0J6teWLYjq9hMFdU1NjFCpvGxx2bRjzV60CS2b1hYVGo6JjcNCp9EZjm2VLtvFMqcF7lzckeE5k2atlFtPJztsWjkd7VtnvgsXAGhpasq0vbh7SG4Banka2w7CC9f38PMPwox562Se20LWeXHr/LYMx0gfhJrqaI/B/Tsr7P/B/bNot0KpNBF9h07H3Us7sXbzQSxetVOYf2BQKJp2GIKxw3pj2jh7aGtrCecpeo5Onr2OcdOXwdvHP8N5/z969Tpr6gb9jPNH1ihdNDyrzFqwAQtXbBe11axqg6unMs/GVJWyWUeqCAtLF1DSFweUFs0ejWqVy+L5q3dYtXE/Dp+4BKk0EWVKFhZel2/cPGSCk5FRMahRpRxWLZyo9Fy6tG+KLumWWKdXvEo7eH3xFbU9eKx4ZzgiIvp3MaBERPQXq1S+JDrZNpZpP5NmuVP6DwkxMbEITfcBJNU3OQWn5S1Vy8jQAV3kfiA8o8QSrPSmjbOXKSaekCAVFQcODYtAv6Ezcen4emhoaOCF63vYlCkmd5ejjGhra2V6jqmxbLZUdtHR1hbtzKeq9FkMGZFI1IWfV23cjwZ1q6J5o5p4+94TBfPn/qmd3jLLrtFIkyGTytvHH2Wqd0JEpHg3qkLWeVGzmg1KlyiEmNg4UUBJnsTEROS2ygHHEX3g+sYd7h+94Onlg2/+QaLMiYx0HzBJJjBbtlQReH7+Knc5U+tmdXDt1iNRIWsAGDusN/p0V243tT/lzoOfD5qeOHMdU53XZNrv9YPfkwn0X2Jv115hAXoA8PL2w4Ll2xCS7v3czORHhmj92pVRrXJZAED5ssWxbc0svPvgiacv3GBTprjQ7+XrD78l2y0rLZvriPDwqEz7lS5Z+A/MhoiIlMGAEhHRX6pwwXw4tH2RaGkKAHwLCMbW3SeEx+mDDmERiv9BHiVnxyVVghZtmtfFnKnDZNovXL2H56/eKT0OkFIfJv0uYgCwbe8J+Kfbte3+o5dYtGongoJCoaWlCZsyxVS6VnZwfeuOCTNWKNVXXrHc7DRo1Bzs3uSM3g7T8NrlzwQGTE2MkNsyB3Jb5UDhgnlRrHABFC9qjfJli6sc9JRIJKhSsYzMUkkACAuPRFh4BKKjYzF/2TYcOnFJ7hhe3r6iHfQG9umARU6jce/hC7TrOQYJCVLhWOrrYkBvHwwbNx837jwWjoWEhis9b6vissHj3yEwKBQfP3nj3KU78P0WgLnTRwjHFizfhg3bDgNIeS9JH9COiIpSecllWiWqtAcAqKurI0eaHfSyWnRMnExg8m+TWc0ul8evsGD5NvgHiN8P074eJo7uJzp29aYLnr5IWdparkxRof3Vmw9o20K2Htew8fMxZsoSleeekXA5fwdpaWlmWog77a6DRET0b2BAiYjoL1SjSjkc2L4QOS3MRO1JSUkYPm4+otIUk02f4RMfrzgDI0EqlWnT0pJdLiWPQ9+OWOo8VmZpU1BwGEZNWqTUGACgpqaGGRMc5Bb6/fjJG7Pmb5B7XmqxcFW3Bs8u4eFRuH77UXZP46f4BwajWYehf/SaF46uQ7nSRTPvmMa3gGAcPXkZOjraaN+6IUxNZGt7pWdsZABjIwPExcXj+p3M/3yMDPWxbukUdGybElhpWLcqtq2Zhd6DpgFICfxuXjUT6urqKGSdF+ePrMWOfacwadZKhIX/XF2xP+HsxduYs3gzunYQL1+KiorBt3QBjLTy5bFEm+Z1FR5XlnX+3L81i2nzzmMYOXGhwuNh4ZG45/Iiy68bGByaZWPFxKYUkff7Fihqz/V9OW/LJrVRv3Zl0bHFq3YKPycnp2R4xsTGITBI/rxiYuJgnT839m9Z8MvzdVq4UeFyv13r5yAkLAKzF26Eb7r7ISKifxcDSkREf5lRg3tg9pShcgM905zX4sxF8dKypKQk0WN5S4lSaWnKvu1HZbIbkYG+HlYumIAenVvInhsdg+72k2TqZSiS08IMW1bPlLvM41tAMDr1GadwuZ4i1249RKsuI2TaJ47uh1mTBguPP3/xEbIj/gV2g6fBbvA0pfounzdOpl5RkQpt8NU36+sHNWrrgHsPZT+IR3jfFQUbB46ajT0Hzyo97upN+7F55YxM+/n6BeDitfs4cfY6rtxwQWJiym6GY6YsQYvGtdCyaR00qlsVeXLnzHCcU+dvIiAwJMM+VSqUxs4Nc0S7EMbExOLspR9Fib989cOeg2cxZEBnqKunLB3s26MtmjWsgdGZ7HIIAOOG28mtg6bInQfPsW3PCZibGWPx7DGZn5DG6k0HhMLs0+etU3r5X1r1a1eWCWL8i167eaCRrcMfvaaamppK/VOXT4ZHRCEqOkZYxmqgr4ccFqZYNHu0qP+te09EhbnHTF6MMZMXwyKTTDBdHW0UL2qt0tzkMTYykNtuYW6CFk1qQ0tLE53bNcG6LYewbO3uX74eERFlPwaUiIj+EmamRti21gnNGtaUORYXF48RExdi94EzMsciIqNFNWV0dXUUXkNeXZ2MloWUKVkE+7cuELaeTsvH1x9d+k0Qtm3PTN1albBz/RyZreEBwO29Jzr0doTn569KjUX/PYeOX8K8GSOQw9xU1O4fGIyHT1zx4NFLXLnhgheu7+WeHx+fgJPnbuDkuRsAgOJFrVGjSjmUL1ccFcqWQKkShUTLO7fvPZnhfIbad0WXdk2FHcmAlA/2DqNm4/GzN8hj9SNgtXzdHri+dcfiOWOEa1hZ5sDB7YsQHBKW4XUa1K2S6dKntKSJidi25wT09fXQvZNskDcjx89cw7OXbmjVtA5aNUvZFa94EWtRH9tW9VGoYF4AwOETl1Ua/1ecPn8TXfpNkGmvU7MiLh1bn2k/ZYpsO08bhhpVbH59skpauHI7Ll27LzxO//7bpe94nE5Te65bh2bYvm628Dj2e4YSAHzx9kOJNLXMVswfj8IFxe/L053XyZ2HouykP6Vr+2bCFyT6eroYP7IPLly5i3ylf+wQMXvKUIwf2Ud4fOTkZfQeNA2FrPPKZLKZFKjzU8FQIiLKegwoERH9BQpZ58Wp/StkPiAAKcvA7AZPUxi4CQ2LEH0DbWpipLBeRS45wZwvX7/JHbd541rYvdFZbo2lqzdd0H/YLJlaR4r07dEWqxZOFH04T3Xg6AUMH79AtIyP/v/Exydgxbo9qFKhDF6/80BoaATee3yG+0fxdueFrPMqNV5CghS37j3FrXtPhTY9XR3o6+kgf16rTJcj9uzcUqbNyFAfB7YpXkYlj6r1n/6E8uWKo19PW7nHKlcojcoVSgMA3rh9lNmy/nepX6cynt8+KNOuly5Arqifrq52ptcoWbwQalb7cwGlnDnES5bT784Wl+49WjNdVmraAu8en7xFAaUObRqJ+p44cx0Pn7r+0nxTrVi/F5OdVmXar1vH5ti+1inTfr26thI9dn3rLpPlmD6LKiiTQCwREf0dGFAiIspmuS1z4PLx9chtJbtEZ9/h8xg1aREioxQXbX7v8Vkmg6hoofx47eYh01fesoY3cvo1rFsVB7ctlFl2Fx+fgFkLNmD5uj0K55Ne726tsX7ZVJn2iMgojJ68GPsOn1d6LPpvW7b2x+/VxWPrULdmpSwdf/fBM3AYNQcuTzL/4P3xkzcKWedFfHwCQsMjZOqZZeTA0Qto3rgWTIwNERAUIpN19S9LfQ4zE+PnotK4hgb6KF5UP8v6/Y0M0gWUYtJkIAEpS8/SSlus3yNdYDWt6OhYTHRamQUzTFGkUD60b90w036Vy5fKtE9FmxIoX7a4qG3LruMy/fLlsRQ99vENyHRsIiLKfgwoERFlI3V1dezdPE8mmBQXF48xU5ZkuiwHAFzfuKNlk9qitlrVy8sElNTV1VGlYmlR2xdvP/j5B4na8ljlxM71c2SCST6+/ug+YLJK34LblCmGVQtkl6e8fe+Jrv0m4IOHl9Jj/T9xe3QcBfL92hbf7s9Oq9R/zJTF2LDtyC9d879k1KRFGDO0F6Y5r8W08QNlXmMZOXX+JmbOX49+PW2hq6uNUYOVLySfvvZU+lpgGdG1rCZ6fOfiDlSyKSnT757LCyxZnVK8uVzpYmjasIZw7Na9J3j4PeD25PkbFE4XrNaQSOQunf1Vf2LJW3azSBdYDA0VZ3/lsBAfT7tbmqucwH+qhSu345t/EDQ1NUS7D/6s1s3qonWzXy+8DgB9e4gz4SKjouV+iVC5gjg49erNh0zH1tXVRmJiUqa7xxER0e/DgBIRUTbq070NqlcpJ2qLjY1D+15jRVuPZ+TStfuYMKqvqM2uexts2iGuO9GqaW2ZTIljZ67KjOc8bZjM8gPPz1/RtP1gePuoVuR52dxx0En3rfuT52/QqssIpXfA2rRyOnp3ba3weMO6VZXKhiiQL7fSWRN2g6bh8Mk/Vz/mb5E/nxXePTqRYR9lP7RvXjlDqSLb7z58Qvk6XZUa808KDgmTW+w9NCwCVsUbi9raNK+LQzvEBbi9vP3gtHAj5s8c+Vvn+TOu334kLPmzt2svCihdvvYAS9bsEh6nDyh179RC5dpNytDU0oS5mezyQGNDA6X6GSkoCJ1Wx96OCo+lD9x5fv6KUtU6CI/nTB2KcSN+1Ph5+MQV9VoNyPSaqdTU1FAoTXF3ADL1tQqnW84ZGPSjaPxTBUueX735gOXr9qBE0YI4sG0B5i3bigNHLwrF6rOTnp4OurQX7yJ46Pglmbp91SuXhYmxofA4KSlJ4f2mVa9WZezaMAdXbzzE2cu3ceHK3WyvF0VE9P+GASUiomw0Zmgvmba5S7cqHUwCgHsPX+CLtx/y5f2xZKCSTUlMcRyAeUu3AkgJFCyeI94RKjExETv3i7NYclvmkPkAAAD9hs5QOZhUvXJZmXol8fEJ6D5g0l+9nTr9nYaPX4BnL5UrAJ+qRZPamDZu4G+aEWWl5o1qwvvNpSzrp6oGdaqIHru+VZwRpEjJ4gVRv3ZlHDp+CUHB4mBRgXxWouC6VCqFlaUFrAvkRmBQKCqUKyGzzMzH78eyr8R0u3kCKe+nA4Y7ISFBCnMzYxSyzostq2Zi2riBmOa8BkdPyX5hoIyd+08rtQtbq6Z1MG+GbNA1VY9OLWR2fpO33C39DpWPn73Bt4DM6/OZmxnD0EAf7Vo3QLvWDbD38DnYj8i8phMREWUdBpSIiLJJsSIFULRwfpn2Ns3rok6NCpmeb9tjNAAgOTkZsxdvkskGmT7eAb26tIKPbwAq2pSQ2f1t5/7TePvOU9TWsmltYevzVElJSZjiaJ/pfFzfumPqnDXC49RdpNJKkEqxZvHkTMe6dushVm7Yl2m//6rwiCiEhilfDFlXR1u00x8AhIVHIjk5Wekx4uL+7mUj7z0+4+kLN5XOKVWicJbPw8TYUOX6QP8aNTU19O3RFhev3svuqfwRjepVQ71a4npdd+4/VdBbsUIF8mLZ3HFYMHMUrt95hANHL+LA0QsAUpb/puXt44/yZYtj9aJJCsd77fYRAGCdPzfOHJAtkn345GVhaViBfFZCu3X+3KhTs+JPB5SKFsqvVA2lcqWLZnh8UL9OosePn73Gs5fi13DDulXRuV0TUdsRJbNDrXJaiB5//OSt1HlERJR1GFAiIsomhQvK362qaqUyKo+15+BZtGlWF21b1he1FyyQBwXTLbMAgOev3mHSLNkPKPJ2mVNXVxctiVFER0cc0JA3lr6erlJjfQv4Udfpk5cPnrxQLTMFAKxymotqU8XFxWdYhySt4NBwla+Xlao2lM1cy8jyeeNkvuWvVK87vvqqllVmmdP8p55rADK1ej55+Si1U5PXF9+fuh79HhXLl8T9y7tgU6YYGraRze66cPUeFq/ckek4GS2NVFdXR1RUNKzLZr50rnqVcqKd9S5eu4dBShQFB1J2vAzJ5LVsU6YYdq4Xj5eYmIgT524odQ3R9UyNAABaWppo1rAmrHLlEAJKzRrVFPV98+4jXroqrhPk9t4Tbu89kS9PLlw4uhZ5cstu2tCoXjVhR8/0Gy5kVMQ7MzWr2fzybni1a1RAmZJFRG2b02UnlSpRCNvXOYm+xAgJDcf2vacyHFtdXQ0AUCC/uM6c23tPed2JiOg3YkCJiCibmJoYZel4vQdPw7qlU+Rud57WpWv3YT/SSaaORVbPKavGmrd0q7B0TxXpa6L4+QeidrO+Sp9fyDovXj84mnlHBWpWs1E5k+XWvSdo1mHoT1/zV/n5B6n0HKUV4X0XGho//lkxd+kWUXHp/1fpCy0nJsouXUprQK92aJhm+ZUqWVbb1swSPbZWorC7gb6e6HHazBTLXBbpuyMgMFhmy/f06tSsmOHxvHlyZVqrS5FmDWvi0yvldobM6PWkpqaGAb3bYcGsUTJFxk+cvZ5poFNNTbatSLog+nv3T8LPTRuIA+nPX72Du6f8TQmiomPgMGoOihbOjxN7lyss0G+Z0xyjBvfA4lU7UbWi+IuI567vM5z/7zZ6iLgQfWhYBA6f+LFUsVa18ti/bYFMXb+la3aJdjVNTJKtBVWkYH68c/+EhnXFyxSfv8reeyYi+n/EgBIRUTZJv7TsV8XHJ8B+hBO27T6BHp1bok6NCrDMZQENiQTfAoLw6OlrHDx2Eecu38lgTnI+Jf2krByLKO1OX9kpMioadoOnZdgntaCwVS4LNGsozkwJCAyRd4qgepVyMoX6lfUzxbLLlCqi8FgOC1NEx8RmeH6vrq0wY7wD4hMSkJAghYaGBgoWEAdAAoIyvuc/SV1dHe1bN8D4kX1llqEBKUHViTNXyrQnJYmXjxbInxs6OtqIjY0DABgbGaBrh2aiPql1mJo3riWTYXT3wXMEh4TD91sgLMxMIJVKERIajhevP8B58SaYm5ng1IGVomLV8syY4IDK5UuhVvXyQltCghRPnr/J8LyMrFi/F5OdZDNY0+vdrTU2rZguPE5dYlu0cH60aFxL1Hff4XOIiYmDpqYGxgzthamO9jI7iT576YYV68VLndPvhAcAF4+tQ3JyMsxMfxRn9/0WyCVvRETZgAElIqJssufg2d+SwXHv4YtMMwgUcRg1Bw5KLifJTHZm2hD9LlJpIs5fvivT3q1jc3Tr2AxBQWFoXK8azEyN0bxxLZmAgIfn3/OhN1+eXOjUtrFMe2xsHBau3IHte0/KBEnSu//wpWhDAHk+uP/IxMmVwwxxsXFKLXcDfm3JW+r1oqJjkSCVwmnyYHS2bSJaCptWeEQUuvQZL3epaPqaZjktzOB67wg+f/GFmhpQrIi1zO5zqfWCJqbbhTMiMgp3XZ4DAArZtJK51ughPeE8bRgkEomo3T8wGHaDpuHAtoXC75WGhobMUucHj18iOjrjQKAqNDQkyJM7JyIjoxEVHYvY2DgYGeqjXasGon6pu9Z5efth+bo9GDW4h5C1mLrcbduaWehkK66ZBADfAoLR3X6SzO50YeGR+ODhJao3KC/79fSFm792k0RE9FMYUCIiIiLKQO9BU6H5/YOxokLn3/yDZLKR0nvh+l4oovw38P0WiFdvPqByhdJC24NHLzFojDPeu39WagwPzy8yu0ymt27rQeFnZZerKaLKkrdUqRk3GhINhcEk949f0LnveIV1eFwev5Jpy5M7p9zaRgAQGBSK67cfQSKRyGSlnb14G3Fx8QrnGxMbB7V0a+r8A4PRotMwvHH7iDGTF2P7utkKzz90POt3wXO5skdmx7b03n5/7uLi4jHNeS2On7mGHevmwPdbgPC8jp68GDZliosCRAFBIWjfcww+e8lfZrhs7W6sXzZV4XUjo6KxasN+VW+JiIiyAANKREREcnj7fEOtZn3+6DUjIqMz7/R/at6yrXB7p1rR3do1K8ChT8dfvrYy2R7PX73L8HhoWAQGj3HOdJyBo2aLMhfT1wLLiK5lNdHjOxd3yBRLT0sqTYTd4Ol4cGU3dLS1MHvRRixbu0el3QEB4OFTV5mAklQqhetbD6zdcvCndxvLapNnr0KdmhVFu5NJpVJs2HYETgs3imr3pHfv4QvcuPMY9WtXzvQ6iYmJcJy6FAkJUgBA1/4TMWfqUDgOtwMAbN55LMPzN24/gojIKGxeOQPq6ur4+Mkb7XuNFYJ8B45dRN48lpgzVTYL9L37Z+zO4sxXqTQRN24/gm26jKS0jp+5Bvd0hcCfPH+L6o17izagCAoOQ7ueY3D7/DaYmRrj3YdP6Gg3Dh6eiouI79iXUqR7xKDuKFIwn7BULiQ0HLfvPYXToo0Znk9ERL8PA0pERERyxMcnqLxNPf0+N+48xu17qm3lrq2jpXRAKTExCVKpVHisYkwFIaHh8PL2hVWuHIhPSEBUdAzCwiLh+y0ALo9dsXH7EblLqRav2ikKADx49FJ0/PSFm/icpkC05/c6MYFBIeg3bGaGc5q9cKOozoy832fPz18xeIwzvvr44+FTV5njx09fw600z3uUnKDL4DFz4Th1KRKTkpCYmIT4+AREx8TKDUxZFmuU4Zx/h9jv2UAJCVIMGD4Ldy/ugJaWJs5eug2nBRuVzhrrZDcOMyY4oF3rBshtmUNUhD42Ng6+3wLx5PkbrN18EA/SZDQlJydjmvNavH3vic7tmii1JHnf4fPIYW6KNs3roWv/CQgKFu+YuGT1Tjx+9hrjRtihWuWyiI2Nx7nLdzBz3roMs5/kiYiMxj2XH3P65OUj0+f2/WewbdUASUlJSEiQIjYuHlFR0fD9Foizl+5g6ZpdcseOio7By9fi5/fjJ2/0GzYTTepXx/R564Q6VBnZse+UEFgiIqK/h5pOrqoq/pOJiIiIiOjf1Nm2Cd65f5IJdPwJ6urqSErKeKe/tCQSiUxdoeyipqamcvYaERH9tzGgREREREREREREKsnaPauJiIiIiIiIiOg/jwElIiIiIiIiIiJSCQNKRERERERERESkEgaUiIiIiIiIiIhIJQwoERERERERERGRShhQIiIiIiIiIiIilTCgREREREREREREKmFAiYiIiIiIiIiIVMKAEhERERERERERqYQBJSIiIiIiIiIiUgkDSkREvyBXDrNsu3aTBtVhbmYs91gh67zIn9dSqXEqlCuBAb3bQ0dHW+ZYRZsSWLVwotxj8mhra2XaJ38+KxgZ6is13t8qVw4zlC1VFBoakuyeispMTYyU/t34Wbktc2Bw/04oWCDPb70O/XmamhrIn89K6feEP8XYyAAzJw6CVS6L7J6KXFn9vBkbGfzW91Hr/LlRvmzxLB1TQ0OCmlVtkDd3TgAp76NqampZeg0iIvqz1HRyVU3O7kkQEf1NdHS0IVEXx9uTkpMQExMnalu3dArq1aqE6o3tEBEZ9SenCACI8XNB2+6jcPn6A5lj+7csgJWlBeq3ts90nGVzHdGrayvkLdUM8fEJomOdbZtg10ZnWBZrhLDwyAzHscxpjpf3DsNx2jLsPnAGAFC6RGEkJiXB7b2naN7TnddiyZpdAAA1NTVUr1wW9x+9zHSuv8LM1AiaGhoqnRMSFiHznADA8IHdsHjOGBQu3xo+fgFZNcVfUsg6L3aun43h4xfghet7hf0Wzx4De7v2MLWuq7CPjo42cioZLI2KikZQcJiorU7Nirh0bD162E/G8TPXlLuBf4yamhq0tDSzZKy4uHgAgLq6OjQ1VfsdTSs+PgHJySn/rCuQ3wor5k/A2ClL4Pn5a5bMEwDKlS4Kl6t70KH3WJy/fBdASgDRpmyxDM/75h+Epy/cRG01q9qofP2w8Ei8dvOQac+fzwrvHp1ArWZ9ZK7zN8jK501NTQ1vXI7h5LnrmDRrleiYsZFBpnPJ7L1cW1sLHs/O4MtXP9RoYpfpeMoyMTaE77srmLVgAxau2I4HV3YjISEBQx3n49WbD1l2HSIi+nN+/l8tRET/URePrkPVSmVEbe8+fEL5Ol1Fbdv3nETvrq2wZvEk9BkyXWYcYyMD5MppLmr78tUPw+y7oV3rBkrPZ9vuE/jw0QuXjq2XOXZq/0rR4xETFuDIySto3rgm5i7dijxWOWXO+errL3rcvHEtXL3xUG7gRBUD+3aEro42bt59IrQ5Tx8Om9JF0ch2kMIPtdPGD8Sk0f3QrONQ3Ln/TGg3NzOG95tLkEqlSl1fXV0dB49dRP/hs+Qev3R8A0qXKKz8DQHo0nc8Tl+4JdNunT83wiOiRMGkijYlcPfizkzH/PzFByWqtFdpHsoIDQtHbsscOLh9IWo16ysT5FFFnRoVZH63FNl98AxGTlwEY0N9BAaHITExUWFfmzLFMNXRHrWql4e2thZev/XAmk0HcPjkZZm+pw+sQuP61eSOU6l+d7xx+yjTXrBAHowf2QeN61VDzhxmCI+Mwp37z9DDfrJS96KK+rUr49zhNVkylq5lyn3adWuN9cum/vQ4aQN40oREVK1YGns3z0O9VgOQkCBFgzpVlJ7zhm2HMWbKEqX61q1VCdvXOmXY5+ipK+jlIL63q6c2KTV+WrfuPUGzDkOV6psaEM/KMbPSzz5vycnJ2LTjKKaNG4hla/bAPzBYOOb3/mqm181bqmmG7w9xcfFYtXEfnCYPQfPGtXDhyt1Mx1RGdEwsgJT3agDoOXAKdq6fjbsXd2DouHnYc/DsjznmzolhA7uhacMayJcnF2Ji4/D2nSeWr9uDi1fvyR2/VdM6GDusN8qWLoLExCQ8ePwKM+etw8vX4mBV1YplMG/GCFS0KQH/wGAcPnEZsxdtQkKC+O+a7p1aYMuqGWjSbjDuPXyRJc8BEdF/DQNKRERyXL/9CMvX7QEArFwwAf6BwShXuqioT1x8PI6fuQ6bMsVQvXJZ4R/LqerUqIglzmNFbU07DMGjp66Ijf2R7TR9ggOu3nTBPZcXGGrfBT6+AThx9rpw3PWNO16++YDS1TuKxnr94CgcRs/B3QfPhbaAwBB07dAUOjramDN1KOZMlf2QZJCnpvChv0qF0ihYIA9mL1L9g11aWlqaGNC7Pe66vICGRIJC1nkBAP2HzcSNM1twct8K1G3ZH6FhEaLzOrRpiEmj+2HXgTOiYFJajdoOwsOnrpnOYduaWZn2WbB8Gxas2J5pP20tTXz7oDizpnChfKKsKwD4/MUXQ8bOVXhO1w7NUL92ZXh+9sn0+j8jOCQc9iNn48zBVdi6Zhba9Rjz02O9ffcRIyYsEB7PnjIUiYlJcFq4Qabvew8vdLJtjM0rZygM9ABArWrlcfbQakgk6rh47T7CwiLRsF5V7NroDOsCubF4lTgYZ25mjNCwCBw4ekH2XuV8GG5YtyoObl8IHW0t3L7/DJdvPEAOCzNUrVhGpm9WePvuIwaOmq3weJP61dGlfVMsXLEd7p5flBpz98Gz2J3mQ3VazRvVxJFdS5CraENERcfK7ZPWV19/jJu+HNvWzMKcqUMxadaqTOec1rv3n4Sf8+e1RMO6VQEAefPkAgA0rlcNuXKkBMyPnLwCgzw1AQDXTm3C5y++6DdspnD+2UOrERkVI/c6J85cx5wlyr3/nNizXKl+qc5fuQub2l0y7RcTk/nz+TOy8nlLzcBK77PreeHn42euoVFbB+Fx904t0LNzC7TpNkrIXAOA0LBIpYNtx/csy/B48Srt4PXFN9NxgJQMOqlUCq3vWXgenl9Qv7U9xg7rjUtX74v69uzSCv17tcODRy/h8vgV8uWxRIM6lVGvViWMnboE67ceFvUf3L8Tls8bj4CgEJw8dwM5LEzRtEF11K1REQ3bDhSyNi1zmuPMoVWIi0vAsTPXUNg6L8aN6AN1dXVMnfMj2GpqYoQFs0Zi256TDCYREWWAASUiIjn8vgXi8vUHQr2ZDdsOw+XqHoX9r5/ZItM2a8EGIRtFW1sLoZ9vAwBu3n0iyuJxHGGHW/eeYMO2I2jfuiFevfmANZsPyIzn7fMNuunqb0RERCEoOBQAIJUmQpqYCMfhdoiKjsGpczeEfrWql4dEXYJb954gKSlJaLfr3gZR0TG4dvOh3A/ehQqmBIYq2pRElJwPhM9d3yE+PgGOw+2QK4cZcuUww+sHR4XjBnlqotuAibh1bhvse7cXlrkBKR+Qtq6eheNnrmHYuPkyY6c6tmcZEpTIUjI21BcF4uSZNKY/Jo3pn+lY8mhqagg1gYoXKQDXN+4oVqSAcNzz81fs2HdK7rmTxw5A/dqVcfn6A3QfMOmnrq+M67cfYdXG/Rg9pCe6d2qB/UfOZ36SHN4+/tiy6zgAoGPbRjA1McLAUbOx5+BZaGhI0LBuVVy69uMDYK+urTIdc+704ZBI1NGq60jc+v77b2Soj2unN2PymP7YvPOYKOBobmYMj0/eSmXJ5M9nhYPbFyIyKgZN2w/Bs5c/lgj9rjpXfv5BooyK9Lq0awrvr98we9Em0WtOkfatG2LfFsWvg1QZBToBwLpsC3wLSMlY2X/kPDq3a4IRDt2x+8BZvHbzyHDOitiUKSaTOTXU/kfG5rXbj+D1xRdqamooZJ0X128/EmWqGejrIixdMDlVSFi4wiBkevEJslmUqa/B3JY5AAD581ohMioGsbFx8PL2w3v3z0qN/Tv8jufNtsdoWJib4NNnH/j5Bwntc6cNBwBR8MPerj1evv6Auy7PFc5xxrx1+PQTQe6mjWqgVxfx637Tyuno3bV1pufKex92mjwEwI/sqXOX7mDTjqMICQ0X+jSuXw2nD6zCxNH9RQGl/HktsWDmKHh5+6JO8/5Cxlb/Xu2wdslkLHV2RON2gwAAg/t3hpamJmo06QOP74HewzsXY2CfDqKA0vyZI5GYmIRpzlmThUhE9F/FgBIRUQYa16sGiUSCE+du4MGjVwCAAvmssGujM+Yu3SLzrWpalSqUzNK5jHTojjnThona9m/9kUVy694TXLnhAuv8uQEAy9ftxas3H6CpqQH3Z6exZtMBURaIoYE+OrdrgrMXb8Mylzluntuq8NqKlskUr9IOEnV1jB9hh+Xr9mD63HWi44mJiXj7zhM1mtjB/aM4S8Priy96DpyCS9fvZ/iBu0OvsVmWoeS8ZDPmLpEN/qWXNgCYqmCBPHhx55DocZsW9YTHNrW7yHx4lUgkWL1oIvr1tMW+w+cxaMwcSKWKl4RlhRnz1uHjJ28cPHYRIwd1R7484gLctWtUgIaGBItny2YwhYZHiJ4fQwN9LJw1GjfuPBYCEZ3bNcW2NbNg22O0KKiUmbKliuK120chmAQA4RFR2LnvFBbNHoMihfLh8bM3wjFzMxO8TZcFpsjcacNgoK+HDr0dRcEkAL/1+ZZIJDDQ15VpNzM1RoM6lbFywz4YGugpPD85ORnhEeL6axaF6iMqWjZ426JJLRzbvUzh8SYNqstdpjhq4kLUqlZebt0hZZ2+cEtYlievFlCqpg1rwMLcRCaAkSuHOYJCfn4JpiISiUT0mgR+vCc+fOKKeq0GZPk1VZGVz1t0dAyOn7kG/4Bg7N44F3MWbRJ98XDj7mPoaGuhVvXyKFooPwCgRlUbfPXxR98ebYV+T1+8FS0Bu3rT5adqTuXKaS4TUAKAkNBwzJi3Ts4ZKWZPGQrXt+44dPyS3OOpGVnyaipdueGC4JAwmBobitr792oHbW0tzF+2TbT8b9ueExju0A21qpdHgfxW+Ozli1LFC8HVzUMIJqWO27pZXViYmyAwKBS1a1RAn+5t0MthSqb1poiI/t8xoERElIGWTWvj6Qs3eH3xFdL6Uz/MvXv/KcNAR8XyJeS2F7LOK8riAYDl88Zj+bzxAICa1WwwuH9nAMBXH38UqdhG6HfP5QUa2aYsaUhblHvi6H5oUr86enZuiZ37T6NmVRvMmOiAzn3Go3+vdshpYYajp8S1NQb37wRjIwN4efvC7cMnmaUhF46sRWh4JEoWK4gv3n44fPIydu4/Lerj4+uPZXPH4atvAGYv2iRTP6dV0zqYPsEB6Q2174JO7ZoAAGZMTPnmuHrj3nKfr6unNsptTy+1hlJGpo0biGnjBio1XnpfvvqhZefhKF60AJbPG48JM5bD9a0H6tWqhImj+yEyMlrmnEVOo9Cvpy2Wrd0t+vb7d0pIkGLzzmMAUpaZlS4prhtlYW4KICVAkZ7ftyBRQGn9sikwMNDDoNFzhLb9R87DoU8HLJ83DhXrdRcKSmfG3fMLcuU0h6amhqhWiXX+3JBKpfD64ie0aWtrQV9PF4FBoZmOa2ZqhLYt6uPug+e4fe+pUnPJKnVrVsywJpHjcDs4Dldc1Dg0LAJWxRuL2vT1dOH3/oqo7fyVe9i+94TwOMJbHJC4ff+ZsEQ3vS9fv+HAsYs/VfA7KSlJpq6MIibGhljqPBbeX7/B7b0nihbOj+CQMFQoVwJ58+QSfYBPy9jIQJTpl5H0809MTBQCNgN6t8eaxZP+2qLciij7vAUGhaKH/WQschoNI0N9LJ4zBovn/AgKp/7dsGnldNi2bIAvX/0QFR0DExNDDHfoBgAoUjAf5i/fhpevP+DVW3fMWrABPr4/t6nAg8cvMWvBBoSmySACgOjoWCG7UZ6Rg3rAy9svwz6K5LAwhbGRgSjwDABNGtQAAJy9dFvmnEvX7qFksYKoXb0CPnv5Ii4+HsaG4sLlZqbGSEiQIiQ0AlpamlizaBLOXb4j83cmERHJYkCJiEgBiUSCZo1qynxQS11CI2/5hSr6DpmOZ6/e4da5bVi+bg+On7mG7Wtn490HTyxYsR39e9miU9smonNqVrNBjJ+L8DhtRsKte09QrXFvJCYmwq5bG6xdMhnrl01Fl3ZNcebiLXz85C301dfTFT5kACm1LdJm19SuUQFWljmwZfdxTB/vgFPnb6JBnSpygyLrtx1CYFAoQj6Ji1dv3nkMD5+6wqZMMSxds0uo4XH5ujirpUypomjeqKbwuFmjmlBXV4OOtjau3XqIwycu41tAEDLTpH51AD8CJR8/fcW7D59EfVZt2IdVG/dnOpaWlibeuBwTtcXExOH67UfIlzcl42ffkfMICg5DyWIFkZAghe+3QJlxrCxz4O17z98eTFJXV4dty/rC409ePnj20g1d+0+U6Xtg6wIUKZwflev3yHDMvZvnoUObRti+9ySqVy6HNs3rIWcOM+SwMIW2thYKWefFhFF9MUfJ+lvTnNfg2O6l2LZmFqY6r0VoaDi6d2oBh74dsX7rYVFmgYWZCYCUrI5lcx0RHROL9x5eOHvxlkwx4bo1K0FLSxOXrt+Hrq42Otk2QcliBREWHonzl+/IFOT9Hdp0G4lX36+zZ9M8+PkHYtw0ce2Z1y7HsGD5duw+kBKUnTi6H7p3aiEcf/veE85LNiMkLBwmBcQ78CUlJaF54x+vkfTHk5OTYZ0/N5yXbEZkVAzKlS4q2ljg7MXbsG1VXwhaKyu1WLVty/pCIeUC+awAADWq2EBHO2UJbnhEFJbPG4eCBfLAtsdo2JQpjsM7F/+4dzcPnLt8R+41OrRphA5tGik9p09e8pdnNa4nLuCuo6ON/Hkt5faVJzw8UrSELCtk9fNWpUJpOPTtiN6DpuLh09cAAAszY1w4uk60jPrOg2fo2NtRZj7Pbx8UfnZ77wm3954oZJ1X9HeKsuq1HICFStSjSy8iMhqG+uKsvVIlCsHz81eZnVRTmZsZo6JNScyaNARh4ZEYP11cS6tEUWt8CwhGQGCIzLlu32uBFbbOBwC4//AlOtk2wQiHbti25yRKFLPGQLv2uHHnMRITEzF5bH/kyZ0TbbqNVPneiIj+HzGgRESkgHX+3DA1MZJZkqKnqwMAOLBtodzzNu88hpETU46pq6nD3MwY2lpaMv2+fP2G9+6foa+ng/fun/He/TNiY+MQFh6J9+6fERgYKnOOy+NXmLdsKzaumA7LnOboPmASrt9+hNFDeqJmNRshW2T73pPo3K4J+vZoi+joWIybLv5wO3lsf5ibGissRtuvhy2ev3qHD+5eAIDjZ69h2MCuKFuqqMxShLfvPLF49Q4cO30Vj67vxZTZq3H20m2EhkagaaOUb45nzt+gcPevvj3aigJKeaxyQl1dLeW6Z65BQ0Mid7e69N68+yicDwBBQWHIYWEK3e9/Xh2+f8CSKFFTJzEpCcWrtAOQUp8HSMkWS0xMRPkyxfDF208IbOTNkwvePt9ERW/TUqZ+zq/S1dEW1d/ZvvckhjrOk9u3eFFrfPDwynRMUxMjAEC/nrbo19MWIaHh8PMPgt+3QLx954n4+ASMHdoL+w4rV6fp8vUH6OUwFfu2zEcn2x+B0v1HzmPiLPFSLXMzYwApy+TKlvpRDD8qOgajJi3C3kPnhLbUDKyIyCjcvbgTJYsVFI7NmOCAecu2KrXM8VcEh4QJdYviExIQExsnPE6VnJyMyKgooT39sjW3956Yu2QL1NXVEeWTEnRNLd5/8eo97DzwIzsw0ENcK+zOg+do022kcJ9NG9QQLY995/4ZV264CAWfLXOaY/7MkVi+bo8QcFvqPBZPX7hh7+Efz63/97nu2TQXGhrifzKOH9lH+Hns1CWIT0jAgBFOuHLDBXp6OmjRaRi0tbUQGRmNR89eK9xFMqPf1fRc7x+R225sZIBm399rcliYIY9VThTIZ6XSLnK7D56Bw6g5mXdUQVY+bxbmJti3ZT6OnLqCEsUKwsPTG89fvcOS2WMQHBKGJWtkd5isUaUcLh1fj1LVOuDL129y5+jl7atU4fL0PisoxK2npwN7O9ldLC/feIDPXr4Ij4gUsiQBQE1NDQe2LkRcfDyqNOgpOmf5vHFCti4AnD5/E137jYe3z4+dSk1NjKCnpwOPT/Iz4FKDTKamKe9nW3YfR7eOzbBo9hgs+r7sNyg4DONnLEexIgUwbrgdps9dKzxfampqCt/biYiIASUiIoU8PL9g254TmDHBAQePXRQKBpuYpNRvWLxqJ7y8xf+oXjBrFBLSZC7ly2sJ7zfya0UAKVsja2hoiLIzFNm08yiMjAxwZOcSnLt0G7atGmDCqL5o2rAGdu47hYUrdwh9y5QsAolEXQhmDLfvhs27juG9+2cUss6L4QO7Yd2WQ2jbsp7MdQoXzIfO7Zpgwswf3wK7vnHHqzcfMHlsf7lbsMfExOHDx5Qghe+3QJlaQtramkhMlB/ISV80edueEwB+fBhSxcMnr4UlgQBwaPsiUZ2jX5G6m1FFm5J4+uKt0F6qRCG4vvVAhzYNYZRuKUX+fFYwNTYU1TABgI+fv4pqCf2qqOgYWBZLyfJ4dG2vwn5WuSxQolhBfPz8NdMPSsPGzUduqxzw9QuEj18AenRqAStLCyFoUaxIAcyfOVII/mWmWqUyWL14EuLjE3D11kOEh0eiVvXy6NyuCV69cRdlAr597wmr4o0RGRUNTU0NWOfPjb7d22KofRdsXD4Nn7x8hN0NU7OZxo/si8vXH6BDr7EIDglDkwbVsWrhREwbNxD3H77EtVsPlZpnVjA2lF3Gpaam3POUlJSE3QfPQEdbG3aDpwntEolE2BHMonADmfMkEokQtF2yZheWrNmFqhXLCLXR3D9+EeqYlShWEPNnjsSVGy7C8+I8dRjcP3rJ3VWvYRsH0fzHDuuFsqWLot/QlADVx8/eOHT8EszNTIT79vH7sZTKOn9uBAWHZrhV/a/o0bkFAoNCkS+vJaaPH4jcljnQ0W6csBwulbGRAfzeX8WQsXMVFtDPSln5vOXPawnfbwFwWrAB29Y4YaqjPd68+4hCBfKgYVsHRMvZ+U9NTQ0aGhoZvs6l0sQsK1yupakJUxMjrF4ku/FA+15j8dnLF75+AaLsudbN6qBo4fxwnLpU5pzb958CamrIlcMMJYsVRJsW9VC8qDX6DZshLGtM/YJH0dLb2O/t2lqaAFKycRu2dUDndk1RpmRhBASGYP+R8/gWEIxLx9fD1c0Da7ccQse2jTBz4mAULZwf3wKCsWbjftGmEkRElIIBJSKiDMxeuBE9O7fEwD4dhILW5t8/wG7bc0Jm+cW8GSMQl+ab+C/efqjR1A7aWlrweH5GZvwqFctAKpXi+ct3Gc7D3q49po4bCA2JBMPGz8fuA2cQ4+eCVRv3I1+eXNizaR7uujzH/qMXMHJQd9SrVQnHTl9D8cq2qFOzEiaN7ofhDt3w5MVbdLYbB2+fb5i1cIPcgJLT5MEIDArB9r2n0Lb5j+PL1uzG9nWzUb1yWTx4/Erp5xAAgj7eVKk/8OPDkGWxRqLCqIP7d8LE0f1RsFxLUf+ZEwcJW3Sn6tJvAgBg3dIpsDAzER4DQK4cZvj06rxKNVd0dLRRoVwJoeismpoaKtqUxMbtRzB7ylAULphP7nnpd3o6fOJylgaUAAjPUVKy4oyont+L6DapXx0Hty1E32Ez5H4QBVJ2rfP8/FV43Lh+NZQqXkgIKL13/ywsq6lWuWyGczM2MsDR3cugpgbUbNYHb9+lFNvW0dHGrvVzMG/GCDx98VZYtpOQIBUCuFJpSmH3ibNWIjQ8EjMmOGDEwG5CQElbOyX7z+PjF1Gtp6OnrsLYyBBrl0zGgN7t/mhAqW3L+mibZgmiMtTU1ITlUWpqalBTSwkSAYCpiSG+vM64PhgA9LCfjONnMt4FDoCw5ChaTnFveR49e422LeqhZPFCWLhiO/wDQxATGyeqIWdv115uICGVsgXxVaWpqYExQ3rh0IlLcBxuh4kzV2L6BAcc2LYAZWt2Rk4LU/j4BcrNFDQ3M0ZIaMRvyyLMyuft6Qs31Gtlj+TkZAwcNRunD6xCqeKFkJSUhLHDemH2ok0ymYepwazExN+fJQkABgZ6eO/+WZTxVKtaeVw5uRHR37Nhv/r6I49VTiGgPWZoL3z18ceW3bI1lY6dvoZjp3/8PrdpXhd7Ns3D8b3LYVOrC0LDIiD9vgNo6mslPS2tlI86abNxpdJEmR0w+3RvgxpVyqFOi/6oXrksdm1wxoZthzF68mI0rFsFc6YNg7dPSj0yIiL6gQElIqIMfAsIxrXbj9C2RT0hoFTYOi/i4xPw1ddfpr+mhgZiY398U5qUnISg4DDhQ2963To0w7OXbsLyl+iYWBQtnB9a379NTRUTG4fNO49i1Yb9iIxKKf68/8h5uL5xx4GjF7Bs7R6YmxrDytICb995YsyUJcK3zvuPnMf+I+dRrVIZJCenZBA17zhUbiChYd2qaN+6ISbNWinzje+hE5cxbfxArF48CXVb9hfqXaxaOBED+3QQ+m1f64Tta50AAANHzQaQku2i6ENbzWo2Sm01/avq1a6Mxzf2CY810nwAcb1/RGEw6NzlOz8CJ5XKQEtLU1gmVNGmBHKYm+L6rUdyPyzv2zIfxYoUyLRe0Z9gbGSAkYO74+kLN0yYsRxHdi3B1ZOb0MlunMzvso6ONqpULC1qszA3gZ6uDurUrChqf/r8LTLTq0srmJsZY+b89UIwCUhZ0jV8/AK0aVEPw+y7iurAyLP30FnMmOCAMqWKCG1R33eFOn1BNmh56do9ACkZOX+SvOVTAemWqaW3csEE0esIgLA0cLLTKgBAi07DcPv+M7nnR369p/T8ChbIAwAZ1gwqU7IIvgUECUuGGtStitrVy2daNyd9VhAAeL48J6dnil8pyg0AQwd0gbGxIfYfuQDH4XaIiY1Fh95jUSCfFeLjE3B873IEBIagZefhovOKFSmAe5d2wnHqUpnNBrJSVj5vuXKYYWDfjhg9uCfcPb+gcv0eqFShFGZNGoynN/dj7lLxTp3633cgjJaztHnqOPuf3qAgLbtB03D45GUAgIG+HoLT7eano5NSLyo1ePnh45eU+lb5LFGnRkXUqGqD0ZMXK1wSmdbpC7ewc/9pDOzTAW1a1MPuA2cQFhGFpKQkYZlseqlfAKVfgppWDgtTzJ0+Ams2H8TzV+9w+sAq3H/4Eo7f66Bdu/UQlcqXwrCBXRlQIiJKhwElIqJMvH7rjj5pliyVLlkErm895O5+pKOjjdg4+YVF06tcoRRaNq2NIWnqh5w8dx2rF01CmFdKIdav32tF7D10Du1bN5T5UJq2qG9awwZ2lWlLW6skbQ2KVOZmxtiyagZeuL7H+m2HZY4nJSXBcdoynNi7HOuXTkXfoTMAAM6LN2PN5gPQ0tQU1VACICxt2Ln/tMIaSgAyDCi9uHtYtGRDT1cH+no6Mh+2DPR18cbto8JxXr91x5zFm4XHpiaG2Ls5pe5Q/db2uHVuG3buP40de08CAPT19XD34g48fPIjm+C1mwfu3H+G3RvnwmHUbDSqVxUBQSEqZ2xlhzWLJyGHuSkcRs3BXZfnaN5xKE4dWInb57ehVdcRokBPbsscuHRM/nLD9O3VGvXK9NpFi6RsY56+SDoA+AcGIyAoBEUK5890nNR6ZmmXEaVmUaVfOgmkFAD+Vxw/cw0fP3lDXV0Nk8b0h4ent7Br4ePnKbtaJSYlZfg6UlaThtUREhqusMA1AHTr2Awd2zZGyaop9XCMDfVFS9ZKlygsFHNes+kA3rl/+qm5/EpRbl1dbUwdZ4+N248gIurHn3V0dCzevvNE04Y1UKZkEfQfPktmnPfun/H6rQemOtpj/9ELSgU0fkZWPW8D+3TAUmdHhISFY8a8ddi08ygSEqR47eaBoyevYMKovnj4xBWFrPMI55iZGCEpKQmRUbKZaMfPXMP7D+KlbqVLFsbE0f0wZ/EmoX5elYqlMWJQd8xasAEfPb1lxnnw+KXws3V+K7x64y46rq+XsiQt9bXo+r0GX/NGtTBt3EA8fOIq7EqpjDfvPABAKLgeGxsHb59vyGOVEwb6esIXLqlKFLUGALx9p/jvhkVOoxEZFYU5i1NqbpUtXRT7Dov/fnn64i3692qn9DyJiP5fMKBERJSJxMQk6H7/llVTUwP1alXCui0HZfqlfhObWkg3M9MnOOCd+2ccOv6jxtKWXcfh8tgVJYsVhG2r+qhW6cdSosvXH2RYPPXgtoVweeKKZWt3yz0e9n0JkTza2lrYs2keTIwN0arrCEil8j+0Xrx6Dxt3HMGgvp0QHROLkRMXwj8wGP6BwUIWVtoaSqkBpTo1KiBRQYZSZtkJNrU6//SSt7QiIqNFtUIszE2EnwODQrF193HYdWuDRSt3IDk5GRN72iIuPgFrNh0Q9WvReRiWznXE0d1LERsbh9WbDmTJkhlDA/3vhZuzPgiyyGk0Otk2waadR3HhSsqW8y9c36NJu8G4cHQdLh5bj7bdRuH5q5Sll/IK9S6ZMxZFCuVDu55jRO2fvHxQrkyxDK8fGBQKAChaSDZoZG5mjBzmpnj3fTemjNSqVh7AjwLsAHDX5TkAoE7NSli2Vrwjo03Z4gDkB7IkEgkM9HURExuX5cGEn6mhdP32I1y//QiD+3eCoYE+Pnh4YeOOI4iJiROyLxQF+VRRqXxJdOvQDCvWK661BQC5cpoLAW0AKJAvtyiT7eMnb6EWkJ9/IJo2rPFT8/mVotwxMXG4//AllqzeBWNjQ5n+U8YOgPvHL0JgLr05izfh9IFVGNS3I1aneZ2n0tfThUSiLrMxgyqy6nm7ePUeYmLjMH/GSCxxHoslzmNFx52XbMa1Ww/RrWMzoa2gdV4EBIXKDUK+cfsoE4A3MEhZCnn4xGVh+Zw0MREjBnXH9VuPREv10tPT00G+PJY4euqqqD21uH/49/fwl68/IDQsAgtmjkRiUhL6D5+l0vtnscIpr6u0RcZv3XuKXl1aoWnD6qIlcgDQtGENxMXF48Yd+dmPDetWRbeOzWHbY7SQtaujrQWddFnF+vq6ouXsRESUggElIiI5jIwM0LZFPairq6Nn55bCt5tdOzSDgb4u9hySXcKR+k1slIKaNOk5L96MyzceyHyYffXmA169+YD8eS1FAaXIqJSAiGVOc0wc3Q/L1u4W/aM6Lj5ld6mg4FCZayUkSBV+KNLU1MC+LfNRt2ZFDBjhhKjoWJltpP3ep3xI8PD8gsoNeqJQgbzo19MW/gHBmLVgQ6b3ev7I2kz7pBcdE4t3Hz7h86vzkEjUhXY1NTVIJBJEeN8V9Q8KCc+wLlHThjXg/kzx0patu09gSP8umD7eAWcu3sSk0f0wZOxcmd24pNJEjJq4CJVsSqJS+VKoU6MC8ljllLsEUlm6utr4+vYSLly9hy59VdvaPSNmpkbYsGwa2rSoh9MXbslst/3e/TNadx2BS8c24PyRtejQayzuP3opt1BvZFQ04uMTfqqA7/HT1zB5TH+MHNIDx75n4gCAlpYmVi5IqWt14uyP7LtK5UvCxzcAvt8ChbYihfIJH6JTC7cDKYGxx89eo3mjmmjRpBbOX075vTAy1Ifz1KEAUpagpWfXrTXWLZ2Crv0m4NR51Wt8ZeRnaigBKUX6p493wJMXb1G9clm8fnAMw8fNh8uTlAy4ph2G4Pa9p3LPVWbr91rVyuPAtoVw//gFS1aLCwyHhIWjQrkSKFm8IIwM9NGoblXRn0mRwvlQrkxR5M2dsoti+lpAqswjK3XtPxGxsXEyAaVWTeugWuWyGDBCccDiyg0XuDx+BccRfbBl93GZbetf3j2EwOAwpbLwFMmq583L2w97Dp7FnKnDMHHmCly4+mOJ49bVM+We07BOFXh8lL/7mTwd2zZCQFCIUrtAAoDztGE4e/E27j96iSoVSkNdXR1PvmfTpcptlRNJSUkIDg0HkPL++eylGxrUqYLRkxfDwzNlfibGhjixdznGT1+ON+8+wq57a2zacUwUDKtXqxL69bRFWHgkzl68JbTv2HsKvbq0wvTxDrh686HwBUSf7m1QtlRRbN55DBGRsn//6ehoY9XCCTh0/BIuXbsvtL999xGtmtXB9LnrEBUdAwN9PbRuWhcvXmVc65CI6P8RA0pERHJER8di86qZMDLUh39gMCY7rYaBvh6mjbPH4ROXhX8Ep5X6TWxkmn+45s2dS2Ex3UdPX2e4REsRMzNjDO7fGbsPnpHZCnpwv04Y3K+TzDkPn7iiXqsBcsdTgxq0tTQxYsJCHDh6Afp6ukLtoyoVS8OhT0eMmrQI0TGxiIiIQmxsHDraOWL+jJEKsxxyWJiiR6cWCPpeT6N4ZVuFhWG7tG+KeTNGyLQ/f/UO5evILt1TlKGUmdPnb8otym2VKwdqVdfB3QfP0WfIdJw7vAYD7Nphx75TCutlTBs/EBXKlcCilTvg0Lcj7l3eib5DZuD67UcqzSlVlYploKmpgVPnb/zU+akMDfShp6cLdXV16Ovp4uHVvbCytMD6rYcwbvpyuR+s37h9RNvuI3Hm4Grk+f6hV1k5LEyRlJSEPQfPYs/Bswr7vXbzwJzFmzFz4iA8urY3ZZe3iEjUqlYe1vlz4/rtR9i440f2SY0qNpg7fThu3XuKz198kT+vJerUqAAdHW0sX7dHCBqlGuo4H5dPbMDhHYtx/spdhIdHoX7tSshtlRNbd5+Q6Q+k1O6Kjo7F5RsPVLpnZfxMDSVDA30c3rkEO/efhoW5CT5qa+OF6zsc2rEIbbqNAqB6hlLePCl/nhJ1dVSpUBqXjq/HazcPdOztKMr6A4Bla/dg2VxHPL15AFKpFC9efxBe30UK5UNOCzNcunYfh3YsFgKC8sjLorxycqNK81aFvIxQDQ0JnKcNwwvX99h/RHbXurSWr9uDA9sWYqBdB6zauF9oL5DfCrmtcmLb9yWwP+N3PW++fuKdNNPWSHJesgUaEgnq1KyI2jUqAACWzx+PyU6r0N1+EkJCwuWO2dm2CRrVq5Zprae0BvfvjNIli6B9zzHo2LYxEhMT8eDRK3Rs2wiJiUlQV1dD/562cH3rIdTkWzBrJBrUqQIAQpANSFluV61yWZiaGkFDQ4Jlc8dh/Mi+uPvgGSKjYlC0UH7Uql4ecXHxcBg1W7SM8K7Lc2zZdRz2du3x7NYBXLr+AJa5zNGkfnW8d/+MmfPlv26mjB0AUxMjmUD7srW7cWjHYjy8tgd3XZ6jVrXysMxlLneHUyKi/3cMKBERySGVSlGzaR/o6mjjvcdnSKWJ2Ld5HnR0dDBx5gq551SvUg5AyjfJqYJDwzFhxgpoampg04rpv33eG7YfgfP3OhBpyav3lCo+IQFtu48Wgg1R0TFCcCAuNh4OfTri4LGLog+gCQlSjJu+TDSOdf7cAIARDt2xfukUvHz9AZt3pdTG+OoboLD2S/pvjrW0NBH08YbC+SrKUEpr5YZ9mOa8FuZmxihexBp5c+dCbqsccJo8BHlz50Ke3DmFGhxrl07BZy8ftOw8HLVrVEBiYiKMDQ1QrnQxNKpXDVdvijMHRg/piclj+sN5yRbMX7YV+49ewP4t84UdutLSkEgy3LI7VY0q5ZCQIMW577Wnfkbe3DlxbM8y6Ovqomv7prh17wlGTVqE0PAIYUc0RZ6+cEO5Wp2FpWkaGhJY5bKAZU4LWFlawDKXBYoVKQAryxw4c3AV8ue1Qt7cOaGrqwOnhRuxYPk20XjGhgYAIPozX7B8G166vsewgV1Ru3p5aGlp4oOHFybOXIG1Ww6J+t5/9AL3H71ERZsSaFCnMsLCI3HnwXNs2HZYqM+V1qs3H1C3ZX84TRqCurUqQk9XB24fPmHesm3YKmf3KCDlOb9y44FMVkp20NCQ4NCORdDV0Ybzks1YMT8lS23pmt24ePU+fL+lbCWvSlHudq0aYMuqmYiNjcPc6SPQpe949B40Facv3JL7fpBavF+eFo1rITo6FnaDp+HOhR3o2LYxXrt5iPq89/DC7oNn5GawLVuzG09eyC/eXryINezt2ss9lp6hoT7gm3m/ujUrwTp/bnTo7Sh6/Rl8390uKU3bqfM38cHDC7WrVxAFlGpUsUk5fu7ns9d+5/OmiNcXX5QsXhB7Ns2F23tPLFyxHcvnj0f9WpXQb9hMuYXYB/XrhEVOo+H23hOLVu0QHUvdRU1LW7xRhJmpEfT1dOHl7QszUyN0sm2Ma7cewccvAJ1tm8C2VQMAQGhYBBxGz4GFuQnWLZ2KNs3rYuvuE8iV0wxjh/VGYFAoVm7Yhzo1Uor9P3/5DhGR0XCcuhRdOzRD3VqVYGJkCP+AYOw9fA7L1u6W+0XMyIkL8d79Ewb0bo9uHZohMCgUm3YchdPCjcKOkWmVKlEIowb3wKhJi+AfKC7YffrCLYyYsABjh/VG1/bN8M79E7r1n5Thkj8iov9XDCgRESmQmoVkoK+HXevnoEHdqmjbbaSwW4zztGHQ19dDZGQUTE2M0a1jM/j6BeC5a0pafGhohLALm0QiQblSRUU1Sa6ekg38pJfaP6eFGUxMUpZ0WOdLCdzkz2slKraqraUJXR1tYVeb9CxzWQCA3A8uWVEDqG6a3b+69p+IS9fuo1fXVjL9KtqUwFD7rkKR1jbN6sI7TaaVmpoaLAo3UHidgXYd4DjCDiWqtMtwPhoaEpQvWwLb1znhs5cP3r77CHV1Ndx/9AJe3n6Iio7BlRMbMXTsXFSqUAov7x5CcjJgN3g6Hj11xYyJg3Bi7zL4fQvCuSt3MH76cqxbOgU9O7fEhm2HMX9Zyo5Kbu89UbFedyQnJ6Nk8YKwbdkAoWHh0NXRQb3alXHngfwAQFo1qpbDnQfPEKwgeyAzZqZGuHl2K3R1dVC/tT06t2uCratn4bWbBy5du48yJYvIzeRQU1ODhoYEEokEGhIJNDQkeO3mgRaNa2GovTg7LDQsAp6fvyIqKgYXrtzFJy8ffP7ii2cv3QCk7O4XExuHBKkUzb7Xhfn46atojHOX7+Dc5TuZ3s+T52/RvONQlZ6D9+6f0d1e8fbraeW0MEPhgvlkdsX6WXcv7pRpk1dofvm88Vg+78eSxtQPuklJyciV0xxd+k2Q2X3R9a27UENJ8v3PKDNd2zfF1jWzcO3WI4yatAhnD63G8zsHcebCLeTLY4nA4FBIE6RIkEohlSZCKk2EhoYEWlqa0NTUhJamBrS0NKGlqYlDJy7BoW8nXL/zCGHhkWjWYQiO7VmG0iUK4fyRtbjz4Bn8vgUhMjIKl67dRyfbxgAAdTV1qEvUIZGoIyQ0HMWLFICRgb5MQLBmNRvUrGaT6T2lcnvvmWmfa7ceonCF1ggOCUeb5nVRpWIZhEdECe9RXl9+RKWSk5NRt2V/maBDjarl8PGTN159LyKtKjU1td/2vO3a6IxdG51F17t17ynU1dXRv5ct5s8cibCwSHTuOx7uH7/gweNX2LN5Hq6f3owpc1Zj/dbDMDczRutmdTFkQBfYlCmGpy/c0LH3WJnfP/fvfw86TRqCfUfOIzExERKJBC2a1AIAPHvhhilj7WFqYoSN21OyDIc4zsPMBRugpqaGT14+KF+mGB7f2A9DfT2MmrQIm3YchamJEU4fXIUFs0bBcYQdzEyM8NrNQwjurNt6COu2HlL6+U5OTsbqTQfk1sKS543bRxjnr63w+JZdx7Fll/xgNBER/cCAEhFRJsYO640ypYqgWYchQtFiAMhjlRPdOjYHAMTFxePVmw9wnLpMyHg4cOyisGQqMTER42ekpNUXss4LAOg7ZDqeZVCToX8vW3Rqm7Jt+LTxA2W2FN+/dYHMOSWKFUSf7m0yvJ+021NLpYkKl6KpavPOYwgJDceRk1cy7BceEYX6tSpDS1sTGhIJAgJDMH1uSo2lQtZ58frBUaWuF/o542ye1GVH+Us3l3s8Vw4zAEAyklGjSjnMX74New6dEwIvw8bNh9OCjehk2xgJUini4xOgr6uLKbNXY/k6cfHn1CwIDYkGpjoOgIaGhrAD06z5GdeYUlNTQ7VKZeG0MPNaVIoEh4Tj46evcFq0UajBdfnGAwy064D2rRsih4UpNCQ/AhGpBaLTFopO/bFDb0fs3H8aXt5+QtDok5eP3G/50ypdsjBKlygMdXV1BIeEYfGqnXB9657hOdmlRtWUjLDzSgS3lNF9wCRRoXBljBzUAx3bpuxulpSUhEZtHRASmnFA8czBVUqN7esfhOeu79FtwERER8eiSoOeGGrfBa2b1UXDelVhYmQILS3NTMcJDApNKQweGytkL/n4BaBOi37o2LYxenZuiT7d2iC3VQ5IJJkHuqbOWS0TUPqVotwZSQ3O5sppjvEj+wBIqQO268AZ3LjzWNRX3u92jSrlfqm2VnJy8m973uTVUNLU0MC9SzthU6YYrt50gcOoOfDxS8ls++Tlg0ZtHbB55QwsmTMWD5+4YoRDd3Tt0AzfAoIxZfZqrN60X+5mDG/feWLO4k0Y3L8z1iz+EbCNio7BiTPXcfD4RXz19Ue+PLmEOYaEhot+l1+++YDDxy9hw/Yjwhc1IaHhaNJuECaN7o/O7ZogJiYWU2avVuUpJiKiv4CaTq6qmefiExH9n9PW1hJqQKSnpqam1LIm+vf9zX/WOjraSu8wSFlDTU0NWlqaCt8bfpaubsqOkb+yHM/YyECmTlJ6GhoSaGpqQFNDQ/jdTv31Tk5OhjQxEbGxcahVvTwePHqlcNmqmpoatLW1IFFPyaxRU1MTloCqqQHJySnjRURGKxzjv+h3PG+FrPPCPyBY7m6QHds2QkKCNMNAWN1alXDr7hOYmRqhWuWyuHz9gcJdPVWR0d+RRET038WAEhERERERERERqUS2gigREREREREREVEGGFAiIiIiIiIiIiKVMKBEREREREREREQqYUCJiIiIiIiIiIhUwoASERERERERERGphAElIiIiIiIiIiJSCQNKRERERERERESkEgaUiIiIiIiIiIhIJQwoERERERERERGRShhQIiIiIiIiIiIilTCgREREREREREREKmFAiYiIiIiIiIiIVMKAEhERERERERERqYQBJSIiIiIiIiIiUgkDSkREREREREREpBIGlIiIiIiIiIiISCUMKBERERERERERkUoYUCIiIiIiIiIiIpUwoERERERERERERCphQImIiIiIiIiIiFTCgBIREREREREREamEASUiIiIiIiIiIlIJA0pERERERERERKQSBpSIiIiIiIiIiEglDCgREREREREREZFKGFAiIiIiIiIiIiKVMKBEREREREREREQqYUCJiIiIiIiIiIhUwoASERERERERERGphAElIiIiIiIiIiJSCQNKRERERERERESkEgaUiIiIiIiIiIhIJQwoERERERERERGRShhQIiIiIiIiIiIilTCgREREREREREREKmFAiYiIiIiIiIiIVMKAEhERERERERERqYQBJSIiIiIiIiIiUgkDSkREREREREREpBIGlIiIiIiIiIiISCUMKBERERERERERkUoYUCIiIiIiIiIiIpUwoERERERERERERCphQImIiIiI/ilNG9bA9dObEfTxJr68voitq2chj1VO4XiVCqUR4+ci89/FY+tkxtLQkGDM0F54cnM/Qj7dgu+7K9i8asafvB0iIqJ/kppOrqrJ2T0JIiIiIspeTTr0gI6ufnZPI1M2xfNgsn0zvPzgg+su72BsqIsOjSsgNi4BjouPIkGaiEql8mPigKZYf/AWgsOihXMjo+Pg8SVAeCyRqGPigKYoViAnzt9+DY8vAdDW0oCJoR7O3nLNjttTSWxMFC4f25fd0yAiov9TGtk9ASIiIiLKfjq6+tAzMMzuaWSqWe2yCI2IxeqD95GYmAQACIuWYkzPuqhiUwTP3/kgh4UJAODVx0BExyaIzk97j7b1S6N0YSs4bbwMb/8whf2IiIhIFgNKRERERCRISkpCbHRUdk9DodjYWMTExSMi7EcAKDBIFwAQER6B6MgIaGsA8QlSRISHI0GaiKQk2YR8TQ0JmlQrikv33uL9R28AgJqaGpKT//7kfR09fairs3IFERFlLwaUiIiIiEgQGx2F03s3Z/c0FPr8/AauntqEagW1sHbzQeS2yoGp/abgxp3HmO80G8nJyahddBS0NKti9/y+AIBPXj7Yuf80lq7ZhYQEKQCgXq1KMNDrh8P7D6Jri3po3awuNDQkuPPgOcZMWYy37zyz8S4z1qbnQGZQERFRtmNAiYiIiIj+GS9ff8DoyYuxZdVMOA63AwB8/uID2+6jhOyiA8cu4uqth4iNi0ceq5zo2bkFZk4chNIlCqH3oGkAgJLFCwIAJo3pjys3XNCl3wQUKZgP08YPxKl9K2FTpwuio2Oz5yaJiIj+AQwoEREREdE/w657G6xfOgW7D57B8TPXYKCnh5GDu+P6mS1obDsIPn4BePbSTXTO/iPncXDbQnSybYJ5y7bi7TtPGBkaAACu3nyI8TOWAwAu4T78A4Oxe+Nc9OjUAlt2Hf/j90dERPSv4OJrIiIiIvonGBnqY8mcMTh66iocRs3B+ct3cfjkZTTrOBQSiTpmThqs8NwDxy4CAMqUKAIAiI9PKdZ99tJtUb8rN1wAACWLFfwdt0BERPSfwYASEREREf0TihUpAEMDfdx1eS5qj46OxdPnbqhcoZTCc7W1NFP6xqQsY/Pw/AIAMDUR1yKSSFL+eZy6gxwRERHJx4ASEREREf0Tvnj7QSqVola18qJ2PT0dVCpfEp+8fAAAmpqyVR369rRFdHQs7j96AQC4efcJoqNjYde9jahfu1YNAQC37j39DXdARET038EaSkRERET0T/gWEIxNO45iqH1XJCRIce7ybejp6WJwv87ImcMMiwZtBwCcPrAKj5664tlLNxgZGsCuW2vUqGqDsVOXIDgkHAAQHhEF5yWbMW/GCOzdPA/Hz1xDsSIFMH5EH1y79VBmKRwRERGJMaBERERERP+McdOX472HF/r1tEX71g0QExuH+49eYuTEBXjy/C0A4K7Lc/Tq0hLDB3ZDglSK56/eoWNvR5y7fEc01vJ1exARGYVh9l3RulldBASGYMP2I3BauFHYMY6IiIjkU9PJVZV/WxIRERH9n2vTcyD0DAwRHRmB03s3Z/d0KAP8syIior8BaygREREREREREZFKGFAiIiIiIiIiIiKVMKBEREREREREREQqYUCJiIiIiIiIiIhUwoASERERERERERGphAElIiIiIiIiIiJSCQNKRERERERERESkEo3sngARERER/bfELnbJ7in8tXTGV8vuKRAREWUJZigREREREREREZFKGFAiIiIiIiIiIiKVMKBEREREREREREQqYUCJiIiIiIiIiIhUwqLcREpq0qEHdHT1s3UOsTFRuHxs3x+5Vh6rnOjaoSmWrd2j8rk5Lcxg36c9Fq/aiYQEKTQ1NZCQIAUAqKmpYe/meRg5cSECg0KFtuTk5KycPhEREREREf1GDCgRKUlHVx96BobZPQ2VXTy2DnVrVlKqr0GemkhMTAQA5MppBrtubYSAUifbxhjh0F3hudv3nsSOfacAAAP7dkBn2yaYt3QrAGD/1gW4fP0BNm4/AnV1dbRv3RCTnFYBQaEY3L8T6tWsjO72k4SxVi2ciJZNaiMwOFTh9fLmzoVZC9Zjy67jSt0bERERERERZR0GlIhUlJSUhNjoqD96TR09fair//wK1dUb92Ppml0Kj1evUg4Hti3McIyLV+/j4dPXCo+HhUUAAHR0tGFv1wEbth2GtrYWAMB58WZcPrEBr908cP/hS+GcKhVKY8HMUXCctkxmvIdPXXHP5YXC63WybZzhfImIiIiIiOj3YUCJSEWx0VE4vXfzH71mm54Dfyk7Kio6Bt8CghUeDw4NF34eN9wOc6YNEx7H+Lng8xcflKjSHhGRmQfSJo/pD8uc5pg1aTBmTRoMADApUAdOCzZi0uh+sO0xRui70GkUlq3dg627ZbOMvnj74emLtwqvU79O5UznQkRERERERL8HA0pE/wc0NTWgr6er8Liujrbw85I1u7B8/V449O2AQX07oVL9HkrXN8qf1xKjBveA3aBpOHzystBernRRxMXH48KVexjQux0AoFuHZjh/5S5CQsNRt1Yl3Lr7RDRWu9YNUKtGhQyvdeHKXaXmRUREREREWS9/Piu8e3RC4fFPXj4oWbU91NTUMHxgVwzo3R7W+XPDzz8Q+49cwIIV2xEXFy/0dxzeG87ThsuM47xkM+Yu2fI7boF+AQNKRP8HHIfbwXG4ndL9ExMTUbdGJeFnABkGpFJ5+/hjyZpd2LXRGbs2OgMAjp+5hqfP36JD20Y4ee4G9KGLWQs2QE1NDWpqamjboj78/ANx6+4TdOvQDAYGekhMSsKh45fw+YuvwmuVKVUE+fJYwt6uPT55+eDKDRel74+IiIiIiH6df0Aw2nYfJdNuamyErWtm4uipKwCAWZMGY8Kovli/9RBu3n2CijYl4TjcDgUL5EHfoTOE83JYmCIoOAz9hs0Qjefh6f17b4R+CgNKRP8HFizfBqeFGxUer1OzIi4dWy881tPTQZOG1eH99RuKFs6PxMQkvH5wNNPrVGvUC86LN+PEmeu4fGIDrMu1RGJiIkYP7gnXt+5YuGK7zDnjhtuhWNECAFKW3sXFJ8hkK8lz886PPhERf7amFRERERERAbGxcbh8/YFM+yKn0QgJi8CilTsAAPZ27XH01BWMnboUAHDy3A1oaWli5KDuGDlxIcK//3s+h7kZvH2+yR2T/j4MKBGRjO4dW8A/IBh6ujo4d2gN1m09BF3LasJxYyMD+L2/CstijRAWHilzfnxCAgCI0le1tbRgbmYs01dXT0f4+dK1+wCAo7uXoniRAgj9Pra2liaKF7HGyzcfhL65c1lg5/7TGQbKiIiIiIjozypYIA8c+nbEJKeVQqAoLi4ewSHhon5BwWGQShORIJUKbRbmJggMCoGeng6io2P/6LxJdQwoEf0fUKWGkrq6OkY4dMOeQ2fRpV1TdO0/EQudRmHd1kPo17Mtjpy8goSEH2/6TRvWgOfnr/jg4QUDfT20aFwLVpYW0NTUQGfbJimd1NTQpX1TdGnfVO71dx88I9M22WkVTl+4BQAoZJ0XV09tQu1mfYXjU8fZQ0MiUeVpICIiIiKi32zWpMEICAzBll0/Nt6Zs3gzls11xD2X57h26xGqViqDscN6YfHqnYiJiRP65bAwRYVyJRD08SakUilevnbHinV7RPVZ6e/BgBLR/wFVaij17NISOjpauHztAbq0a4p3Hz6hXY8xqFurEubPGImTZ2+IAko9O7eEpqYGethPhrq6GrR1tKClpQU1pPycavfBM3AYNUfmemmXvKXlPG04xo3oAwDQ0tKEmYkRbp7dKhzPY5VTbiCKiIiIiIiyh3X+3OjQpiGmOa+FVJootG/fexJVK5XB9nWzhbbzV+5i/rJtovOnz10HAEhOTkbRwvkxpH9n7NroDBMTQ2zeeezP3AQpjQElov8DqtRQioyMxjTntUhMShT1cRzWGzsPnIbvt0AYGxkI7fOWbcXj63tRvmxxPH/1DnsOnkWxIgXgOLw39hw8CyAlaNS7a2v07tpa7vXlBYau3XqIF67vAQAW5qYoOCIPtu89KRxv1ayOkndPRERERER/wuihPREZFYOtu0+I2jetnI6ObRrDeclmuDx+hULWeTFpdD8c2bUEHXs7IikpCQBw9eaPjXau3XqI/UfO4+G1PZg6biADSn8hBpSIVKSjp482PQf+8Wv+Kacv3IRUmoiKNiWEtlrVy6NuzYoYMWGBTP93Hz7h1PmbmOJojy59x8scb9+6IQDg0PFLGDt1iczxofZdkS9PLlHblRsPEBUdC1+/AABAYlISEhMT8dXXX+hz+95TeH7++nM3SUREREREWcpAXw+9urTCnkNnERkVLbTXq1UJvbu2xogJC9Isg3PB3QfP8fjGPnTv1Bx7D52TO2Z4RBTOX76Lwf07I1cOM3wLCP4Dd0LKYkCJSEXq6urQMzDM7mmoRF9PF7lymCk8bmZiJPycNjUVANTU1LDIaQw27jgCL28/ueevXL8P189sRtlSRfHqzQdIJOrQ0JBg35b5aNG4FuYu2YK4+HgEBYfJnBuTptieVS4LHN+7XKaPlpYmDA30MWfqMJljg/t3RqsuIxTeGxERERER/X5tW9aDvp4uDp8Q1zuqVL4kAOCuy3NR+2s3DwQFh6FyhdIKA0pAymcBAIhOU2uJ/g4MKBEpKTYm+7em/9k5jBjUHSMGdf+pcyUSdezcfwqHT1xGmZJFEJ+QgIo2JZGYmIjomJRg0MOnrhg9eTG++QcBAOrXrgwDfT0YGuijehM7tGlWVzRmTgszGBrqI0EqRbUqZeHtk5J55PstENUb95aZQ2pRbnnHiIiIiIgo+3Vu1xS+fgG4++C5qP2Tlw8AoFa18nj7zlNoL1OyCMzNjPH5+3E1NTWoqakJy9+AlCLdrZvXxaOnroiIzP7PYyTGgBKRki4f25fdU/hpqtRQSk8qTcSmHUcBAFtWz0TLJrURExOLNZsPiIpzp13TfOrcTcTExGHHvlMAIBNQalivKpY6j4WGhgYCg0KwYHlKMb47F3fI3blNS0sT5qbGeHBlt9w5Pn3xFkMd5ym8PyIiIiIi+n3U1dVRu3p5XLp2X+bYqfM38eTFWyycNRp5c+fCs5duKJDPCqOH9sLnLz7Yuf80AMDIUB+3z2/Hjn0n4eHpjQL5rDB8YDfo6+nCceqyP31LpAQ1nVxVk7N7EkRERESUvdr0HAg9A0NER0bg9N7NvzRW7GKXzDv9n9IZX+2Xx8jKPysioqxQrnRRuFzdg6lzVmPZ2j0yxw0N9DHFcQDatWoAq1wWCAoOxfkr9+C8eBP8vq9y0NLSxO4NzqhWpRzMTIwQGh6B2/eeYu7SLXjj9vFP3xIpgRlKRERERERERPTTXr7+AF1LxQHziMgoTHZahclOqxT2iY9PQNf+E3/H9Og3Uc/uCRARERERERER0b+FASUiIiIiIiIiIlIJA0pERERERERERKQSBpSIiIiIiIiIiEglDCgREREREREREZFKGFAiIiIiIiIiIiKVMKBEREREREREREQq0cjuCRDRn6ejo43Y2LjsngYREREREf0BsYtdsnsKfzWd8dWyewr/JGYoEf2fKVIoHzyenUZFmxK/7Ro5LcwwxXEANDVTYtap/wcANTU17NsyHxbmJqI2IiIiIiIi+ncwQ4lIBdkZ2c+qqLn7xy+49/AFHPp2wuAxznL7DOzTAcaGBsLjOw+e4eOnr6LAUHqBwaGIi4tPOb9vB3S2bYJ5S7cCAPZvXYDL1x9g4/YjUFdXR/vWDTHJaRUQFIrB/TuhXs3K6G4/SRhr1cKJaNmkNgKDQxVeL2/uXJi1YD227Dquyu0TERERERFRFmBAieg/qmrFMrh5bmuGffp0byPTNt15Lez7tIeHpze++QeheeNakCYmYs2SyShdorDCsTr0Hovzl+9CR0cb9nYdsGHbYWhrawEAnBdvxuUTG/DazQP3H74UzqlSoTQWzBwFx2nLZMZ7+NQV91xeKLxeJ9vGGd4bERERERER/T4MKBH9h0mlUpSo0h4amhJoSDQyrZu0c/0c4eclq3fB9a07OrZtjDMXb2HF+r1KXXPymP6wzGmOWZMGY9akwQAAkwJ14LRgIyaN7gfbHmOEvgudRmHZ2j3Yuls2y+iLtx+evnir8Dr161RWaj5ERERERESU9RhQIvqPCgoJw4mz1/HV1x99e7TFjAkOmLt0K7btOYHk5GS0b90QCQlSnLl4Szjn6q2HeOf+SXjctX0zPH3xFu4fvyh1zfx5LTFqcA/YDZqGwycvC+3lShdFXHw8Lly5hwG92wEAunVohvNX7iIkNBx1a1XCrbtPRGO1a90AtWpUyPBaF67cVWpeRERERERElLUYUCL6j/Lw/ILeg6YBAHbsOwWJRIL5M0fArltrNGgzELYt6yMsIlIUUJq/LGWJ3OI5KVlE3Tu1wM79p7Bp5XT07to602sWr2yLJWt2YddGZ+zamFKf6fiZa3j6/C06tG2Ek+duQB+6mLVgA9TU1KCmpoa2LerDzz8Qt+4+QbcOzWBgoIfEpCQcOn4Jn7/4KrxWmVJFkC+PJezt2uOTlw+u3ODOFURERERERH8KA0pE/3EN61bFB4/P2Lr7OK7deoimDWsgKSkJGhoSJMRLMzz33QdPNG1YA/2HzcR057VCe7vWDdGvpy1su48S9fcPDIHz4s04ceY6Lp/YAOtyLZGYmIjRg3vC9a07Fq7YLnONccPtUKxoAQBAcGg44uITZLKV5Ll550efiIioTPsTERERERFR1mFAieg/rpNtY3Tv2BwbdxzBwhU7sHH7EQCAoYE+Pnh4ZXjuZKdVcHt0AsWKFMDjZ2+go6ON2Ng4hIVHQpqYiG8BwdDU1EBCgjgwFZ+QAADCrm8AoK2lBXMzY5lr6OrpCD9funYfAHB091IUL1IAoeGR38/VRPEi1nj55oPQN3cuC+zcfxpOCzeq8nQQERERERFRFmBAieg/bqjjPJy9eBvL542Dhbkp7Ec4AQCsLC3gfzU4w3O/BQTjyk0XtGvVAE9fuOHV3cPoO3SGqM/9y7vgOHUpbt59AgN9PbRoXAtWlhbQ1NRAZ9smKZ3U1NClfVN0ad9U7nV2Hzwj0zbZaRVOX0hZjlfIOi+untqE2s36CsenjrOHhkSi7NNAREREREREWYgBJaL/KH09XRjo6wIAHj97jeadhsHQQA+5cphBQ0MDRQvlR3BwGHLlMBOdJ01MFD1+9dodBQvkRqN6VaGtowWXJ6+QL6+lcPzG7ccYObgHbt59AnV1NWjraEFLSwtqSPk51e6DZ+Awag7SS7vkLS3nacMxbkQfAICWlibMTIxw8+xW4Xgeq5xyA1FEREREf7v8+azw7tEJhcc/efmgZNX2wmOJRAJ7u/aYM3UochZpmOHY2tpaeHbrAAoWyANdy2pZNWUiIhkMKBH9R40e2hPTxg3MsM+O9bIBHg9P8Y5ulrnMERMbh95dW+PAkQuQSsUBp7VbDuLl3UMoWjg/Pnh4Yc/BsyhWpAAch/fGnoNnAaQEjXp3ba2wsLe8wNC1Ww/xwvU9AMDC3BQFR+TB9r0nheOtmtXJ8N6IiIiI/lb+AcFom64WJQCYGhth65qZOHrqCoCUL9A6t2uCgX06oJB1XqXGHjO0FwoWyJOl8yUikocBJaL/qLlLtmDuki0y7ZXKl8SZg6vh+sYdXftPQHBIuEwft0fHMWZoLyyb64hC1nkxxHEu1i+diqqNesn09fz8Feev3MNwh24YNXGRzPH2rVO+RTt0/BLGTl0ic3yofVfky5NL1HblxgNERcfC1y8AAJCYlITExER89fUX+ty+9xSen79m8iwQERER/X1iY+Nw+foDmfZFTqMREhaBRSt3AABGD+mJ3t1aY92WQyhSKB86t2uS4bhFC+fH+BF98OylGyqUK/E7pk5EJGBAiUgFOuP/3bRhLS1NjBzUAxNH98Xp8zeRK6c5Pjw5jb2Hz2H1pv0yBbr3HTmPsPAIvHr9AWOG9sK124/w7sMnuWNv2HYYh3YsgtOCDQgOCYdEog4NDQn2bZmPFo1rYe6SLYiLj0dQcJjMuTHRscLPVrkscHzvcrlzNzTQx5ypw2SODe7fGa26jFDx2SAiIiL6uxQskAcOfTtiktNKhH/fwXbD9iOYuWA9oqNjsWnl9AzPV1dXx9bVM3Hp2n24urkzoEREvx0DSkT/YdraWqhSsTQ6tG6Izu2aIiYmFiPGL8CBYxcBAFUqlMaE0X3x7NYBXLx2H6s27MPNu08AAN/8g3D99iMAwOPnb3H01FVUq1QGkVExaNW0NsK/78AGpCxPmzBjBaK+B4fq164MA309GBroo3oTO7RpVlc0r5wWZjA01EeCVIpqVcrC2ycl88j3WyCqN+4tcx+pRbnlHSMiIiL6L5g1aTACAkOwZddxoS19KYKMTB1nj6KFC6BL3wkYYNc+8xOIiH4RA0pE/1HD7Lti7vThkEjUcfv+M0yYuQJHT11BfHyC0OfRs9fo3Gc8ypQsggmj+uLsodVYsGK7zFj7j5wHADy5uR+lihdCQFAIBo92FvXZtueE8POpczcRExOHHftOAYBMQKlhvapY6jwWGhoaCAwKwYLl2wAAdy7ukLtzm5aWJsxNjfHgym659/r0xVsMdZynxLNCRERE9Pexzp8bHdo0xDTntTL1KpXRqF41TBzVF/2HzYKff9BvmCERkSwGlIj+o/YdOY8vX/1w485jIW1aEde37rAbPA2zF+VDWHgknBdvltuvUr3uSl37q6+/EEwCgCVrdomOHzh6AQeOXpA5r3azvkqNT0RERPRfMnpoT0RGxWDr7hMqn1uwQB5sX+eELbuP49CJS1k/OSIiBRhQIvqPCgkNx6nzN1U6x/2j8mnVRERERPTrDPT10KtLK+w5dBaRUdEqnWtqYoTje5bh0+evmDV/A/T1dAEAWpqaACA8joqOydpJExGBASUiIiIiIqJs07ZlPejr6eLwicsqn9uyaW0UL2oNAPB9d0XmeODHGwAAXct/d2MZIvp7MaBERERERESUTTq3awpfvwDcffBc5XMvXbuPRm0dZNrturdBn+5t5B4jIsoqDCgRERERERFlA3V1ddSuXh6Xrt3/qfMDAkMQEBgi096gbhUAwL2HL35pfkREGVHP7gkQERERERH9PypTsjAM9PXw5Pmb7J4KEZHKGFAiIiIiIiLKBi9ff4CuZTUsW7sn074Oo+YoXQtp7pItrJtERL8dA0pERERERERERKQSBpSIiIiIiIiIiEglDCgREREREREREZFKGFAiIiIiIiIiIiKVMKBEREREREREREQqYUCJiIiIiIiIiIhUopHdEyAiIiIiIvobxS52ye4p/NV0xlfL7ikQUTZihhIREREREREREamEASUiIiIiIiIiIlIJA0pERERERERERKQSBpSIiIiIiIiIiEglLMr9D2nSoQd0dPWzexqkotiYKFw+ti+7p0FERERERESUZRhQ+ofo6OpDz8Awu6ehlLy5jNGufhkUL5ADOtoaCAmPwcajD+DhHQQAKJTHDDMcmsic5+bpjwU7rguP61YsiPqVCiOXuSHUAHj7h+H0rTd45e73p26FiIiIiIiIiNJhQOkflJSUhNjoqOyehkI2xfNgfL/GeP/ZH7tPP0BkdBxymBkiMjIC0ZERAABtiSkAYP3BWwgOixbOjYyOE/oAgE0RS7i8/Iiv/mEw0NNG81qlMLpHHczZeB5vPHz/7I2pSEdPH+rqXFVKRERERERE/z0MKP2DYqOjcHrv5uyehlxmpkZYO+UQjp26gn7DZiru170NgKZwmu6EsPBIhf3S3+eK/FZwe3gCFhoRf+1zkKpNz4H/TEYZEf1/KFOyCKaNs0et6hVgaKCHr77+6D9sJlyeuMrtv3P9HHRp3xRNOwzB7XtPhXZjIwPMmOCA9q0bwtTECO/cP2PtloPYfeDMn7oVIiIiIspmDChRlurfsx30dXUx2WkVAEBDQwKpNFGmXw5zU8TGxkEqTYREIkFiomwfeRLipQCAb/5BWTdpIqL/A43rV8PhHYvh8sQVU+asRkhIOArks0J8glRu/1rVyqNL+6Zyjx3fswylSxbBopXb8fHTV3Ro0xCbVkyHpoYGtu058RvvgoiIiIj+FgwoUZZq1qgmXN+6o3aNCpg9ZSgKFsgD/8BgrNtyCAtXbBf65bAwhY6ONgI/3gAAfPLywc79p7F0zS4kpPtwo6+nC309XZQoXhAzxjvg1ZsP2Lzr2J+8LSKif5qZqRG2rXXCibPXM8weTaWtrYXViyfh6Qs3VLQpITpWpmQR1Khqg1GTFmHTjqMAgONnrqFggTy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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from matplotlib.patches import Patch\n",
    "from faker import Faker\n",
    "\n",
    "# ===================== 1. 目标汇总表（与 Excel 一致） =====================\n",
    "\n",
    "summary_target = pd.DataFrame({\n",
    "    \"商品\": [\"口红\", \"面膜\", \"隔离\", \"防晒\", \"精华\", \"面霜\"],\n",
    "    \"实际销量\": [653, 523, 648, 856, 714, 785],\n",
    "    \"目标销量\": [700, 500, 600, 900, 600, 600]\n",
    "})\n",
    "\n",
    "# ===================== 2. 生成模拟 erp_order 数据 =====================\n",
    "\n",
    "def random_partition(total, n_parts, rng):\n",
    "    \"\"\"\n",
    "    把 total 拆成 n_parts 个 >=1 的随机正整数，保证总和不变\n",
    "    用来生成每个订单的 quantity，使得汇总等于指定“实际销量”\n",
    "    \"\"\"\n",
    "    assert total >= n_parts > 0\n",
    "    cuts = sorted(rng.sample(range(1, total), n_parts - 1))\n",
    "    parts = [a - b for a, b in zip(cuts + [total], [0] + cuts)]\n",
    "    return parts\n",
    "\n",
    "\n",
    "def generate_erp_orders(summary_df, n_orders_each=200, seed=42):\n",
    "    \"\"\"\n",
    "    根据汇总表，为每个商品生成若干条订单记录：\n",
    "    - quantity 按随机拆分，保证汇总 = 实际销量\n",
    "    - 其他字段按 Faker+随机规则生成，遵守 erp_order 的字段类型\n",
    "    \"\"\"\n",
    "    rng = random.Random(seed)\n",
    "    Faker.seed(seed)\n",
    "    fake = Faker(\"zh_CN\")\n",
    "\n",
    "    rows = []\n",
    "    id_counter = 1\n",
    "\n",
    "    # 给每个商品设一个“基础单价”区间\n",
    "    base_prices = {\n",
    "        \"口红\": 139,\n",
    "        \"面膜\": 89,\n",
    "        \"隔离\": 129,\n",
    "        \"防晒\": 159,\n",
    "        \"精华\": 299,\n",
    "        \"面霜\": 259\n",
    "    }\n",
    "\n",
    "    stores = [\"旗舰店A\", \"旗舰店B\", \"直营店C\"]\n",
    "    platforms = [\"天猫\", \"京东\", \"抖音\", \"快手\"]\n",
    "    provinces = [\"北京\", \"上海\", \"广东\", \"浙江\", \"江苏\"]\n",
    "    cities = [\"北京\", \"上海\", \"广州\", \"深圳\", \"杭州\", \"南京\"]\n",
    "    statuses = [\"已完成\", \"已收货\"]\n",
    "    color_specs = [\"红色\", \"粉色\", \"自然色\", \"清爽型\", \"滋润型\"]\n",
    "\n",
    "    for _, row in summary_df.iterrows():\n",
    "        product = row[\"商品\"]\n",
    "        total_qty = int(row[\"实际销量\"])\n",
    "\n",
    "        # 拆成 n_orders_each 份随机数量，保证数量汇总等于实际销量\n",
    "        parts = random_partition(total_qty, n_orders_each, rng)\n",
    "        base_price = base_prices.get(product, 99)\n",
    "\n",
    "        for q in parts:\n",
    "            # 生成时间：下单 -> 付款 -> 发货，保证时间顺序合理\n",
    "            order_time = fake.date_time_between(\n",
    "                start_date=datetime(2022, 1, 1),\n",
    "                end_date=datetime(2022, 6, 30)\n",
    "            )\n",
    "            payment_date = order_time + timedelta(hours=rng.randint(1, 72))\n",
    "            shipping_date = payment_date + timedelta(hours=rng.randint(1, 72))\n",
    "\n",
    "            # 单价略微浮动\n",
    "            unit_price = base_price + rng.randint(-20, 20)\n",
    "            if unit_price <= 1:\n",
    "                unit_price = base_price\n",
    "\n",
    "            product_amount = round(unit_price * q, 2)\n",
    "            discount = rng.uniform(0.8, 1.0)\n",
    "            payable_amount = round(product_amount, 2)\n",
    "            paid_amount = round(product_amount * discount, 2)\n",
    "\n",
    "            rows.append({\n",
    "                \"id\": id_counter,\n",
    "                \"internal_order_number\": fake.uuid4(),          # 内部订单号\n",
    "                \"online_order_number\": fake.uuid4(),            # 线上订单号\n",
    "                \"store_name\": rng.choice(stores),               # 店铺名称\n",
    "                \"full_channel_user_id\": fake.uuid4(),           # 全渠道用户ID\n",
    "                \"shipping_date\": shipping_date,                 # 发货日期\n",
    "                \"payment_date\": payment_date,                   # 付款日期\n",
    "                \"payable_amount\": payable_amount,               # 应付金额\n",
    "                \"paid_amount\": paid_amount,                     # 已付金额\n",
    "                \"status\": rng.choice(statuses),                 # 状态\n",
    "                \"consignee\": fake.name(),                       # 收货人\n",
    "                \"spu\": f\"SPU{rng.randint(1, 50):03d}\",          # 款号\n",
    "                \"order_time\": order_time,                       # 下单时间\n",
    "                \"province\": rng.choice(provinces),              # 省份\n",
    "                \"city\": rng.choice(cities),                     # 城市\n",
    "                \"platform\": rng.choice(platforms),              # 平台站点\n",
    "                \"sub_order_number\": fake.uuid4(),               # 子订单编号\n",
    "                \"online_sub_order_number\": fake.uuid4(),        # 线上子订单编号\n",
    "                \"original_online_order_number\": fake.uuid4(),   # 原始线上订单号\n",
    "                \"sku\": f\"SKU{rng.randint(1, 500):05d}\",         # 商品编码\n",
    "                \"quantity\": q,                                  # 数量\n",
    "                \"unit_price\": unit_price,                       # 商品单价\n",
    "                \"product_name\": product,                        # 商品名称\n",
    "                \"color_and_spec\": rng.choice(color_specs),      # 颜色及规格\n",
    "                \"product_amount\": product_amount,               # 商品金额\n",
    "                \"original_price\": unit_price + rng.randint(10, 50),  # 原价\n",
    "                \"is_gift\": \"否\",                                # 是否赠品\n",
    "                \"sub_order_status\": \"已发货\",                   # 子订单状态\n",
    "                \"refund_status\": \"未申请退款\",                   # 退款状态\n",
    "                \"registered_quantity\": q,                       # 登记数量\n",
    "                \"actual_refund_quantity\": 0                     # 实退数量\n",
    "            })\n",
    "            id_counter += 1\n",
    "\n",
    "    df = pd.DataFrame(rows)\n",
    "    return df\n",
    "\n",
    "\n",
    "df_erp = generate_erp_orders(summary_target, n_orders_each=200)\n",
    "\n",
    "print(\"模拟订单数据条数：\", len(df_erp))\n",
    "print(df_erp.head())\n",
    "\n",
    "# ===================== 3. 汇总校验：模拟数据 -> 汇总结果 =====================\n",
    "\n",
    "# 依据订单表计算“实际销量”= 每个商品 quantity 之和\n",
    "agg = (\n",
    "    df_erp.groupby(\"product_name\", as_index=False)[\"quantity\"]\n",
    "    .sum()\n",
    "    .rename(columns={\"product_name\": \"商品\", \"quantity\": \"实际销量\"})\n",
    ")\n",
    "\n",
    "# 为了校验方便，按商品名称排序\n",
    "agg_sorted = agg.sort_values(\"商品\").reset_index(drop=True)\n",
    "target_sorted = summary_target[[\"商品\", \"实际销量\"]].sort_values(\"商品\").reset_index(drop=True)\n",
    "\n",
    "print(\"\\n由模拟数据汇总得到的销量：\")\n",
    "print(agg_sorted)\n",
    "\n",
    "# 严格校验：与目标表完全一致（不一致会抛 AssertionError）\n",
    "pd.testing.assert_frame_equal(agg_sorted, target_sorted)\n",
    "print(\"\\n校验通过：汇总结果与目标表完全一致！\")\n",
    "\n",
    "# ===================== 4. 画图：复刻 Excel 深色双柱形图 =====================\n",
    "\n",
    "# 全局中文字体（如果本机没有这些字体，会有 warning，但不影响运行；\n",
    "# 你可以根据自己电脑安装的字体自行替换）\n",
    "mpl.rcParams[\"font.sans-serif\"] = [\"Microsoft YaHei\", \"SimHei\", \"sans-serif\"]\n",
    "mpl.rcParams[\"axes.unicode_minus\"] = False\n",
    "\n",
    "products = summary_target[\"商品\"].tolist()\n",
    "actual = summary_target[\"实际销量\"].tolist()\n",
    "target = summary_target[\"目标销量\"].tolist()\n",
    "x = range(len(products))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 6), facecolor=\"#081736\")\n",
    "ax.set_facecolor(\"#081736\")\n",
    "\n",
    "# ---- 外层：目标销量空心柱 ----\n",
    "ax.bar(\n",
    "    x,\n",
    "    target,\n",
    "    width=0.6,\n",
    "    edgecolor=\"#5f7ea8\",\n",
    "    linewidth=2,\n",
    "    fill=False,\n",
    "    zorder=1\n",
    ")\n",
    "\n",
    "# ---- 内层：实际销量实心柱 ----\n",
    "ax.bar(\n",
    "    x,\n",
    "    actual,\n",
    "    width=0.4,\n",
    "    color=\"#00a2ff\",\n",
    "    zorder=2\n",
    ")\n",
    "\n",
    "# ---- 顶部数据标签（实际销量）----\n",
    "for i, v in enumerate(actual):\n",
    "    ax.text(\n",
    "        i,\n",
    "        v + 10,\n",
    "        str(v),\n",
    "        ha=\"center\",\n",
    "        va=\"bottom\",\n",
    "        color=\"white\",\n",
    "        fontsize=12\n",
    "    )\n",
    "\n",
    "# ---- X 轴：商品名称 ----\n",
    "ax.set_xticks(list(x))\n",
    "ax.set_xticklabels(products, color=\"white\", fontsize=12)\n",
    "\n",
    "# 隐藏 Y 轴与边框，营造“看板”效果\n",
    "ax.yaxis.set_visible(False)\n",
    "for spine in [\"left\", \"right\", \"top\", \"bottom\"]:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "\n",
    "# ---- 图例（目标销量 / 实际销量）----\n",
    "legend_handles = [\n",
    "    Patch(facecolor=\"none\", edgecolor=\"#5f7ea8\", linewidth=2, label=\"目标销量\"),\n",
    "    Patch(facecolor=\"#00a2ff\", label=\"实际销量\")\n",
    "]\n",
    "legend = ax.legend(\n",
    "    handles=legend_handles,\n",
    "    loc=\"upper left\",\n",
    "    bbox_to_anchor=(0.12, 0.93),\n",
    "    frameon=False,\n",
    "    fontsize=11\n",
    ")\n",
    "for text in legend.get_texts():\n",
    "    text.set_color(\"white\")\n",
    "\n",
    "# ---- 标题 & 副标题 ----\n",
    "fig.text(\n",
    "    0.5,\n",
    "    0.92,\n",
    "    \"2022年上半年各商品销量完成情况\",\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    "    color=\"white\",\n",
    "    fontsize=22,\n",
    "    weight=\"bold\"\n",
    ")\n",
    "fig.text(\n",
    "    0.5,\n",
    "    0.86,\n",
    "    \"防晒整体销量最好，达到856，面霜远超目标，超额完成30%\",\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    "    color=\"white\",\n",
    "    fontsize=14\n",
    ")\n",
    "\n",
    "# ---- 底部数据来源说明 ----\n",
    "fig.text(\n",
    "    0.02,\n",
    "    0.03,\n",
    "    \"*注：数据源于公司销售系统，统计日期截至2022.06.30\",\n",
    "    ha=\"left\",\n",
    "    va=\"center\",\n",
    "    color=\"white\",\n",
    "    fontsize=10\n",
    ")\n",
    "\n",
    "# 调整上下留白，使布局更接近 Excel 模板\n",
    "plt.subplots_adjust(top=0.8, bottom=0.15, left=0.06, right=0.98)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7119d0b6-585e-454e-8712-b57504e8c73f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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